US20040165090A1 - Auto-focus (AF) lens and process - Google Patents
Auto-focus (AF) lens and process Download PDFInfo
- Publication number
- US20040165090A1 US20040165090A1 US10/778,785 US77878504A US2004165090A1 US 20040165090 A1 US20040165090 A1 US 20040165090A1 US 77878504 A US77878504 A US 77878504A US 2004165090 A1 US2004165090 A1 US 2004165090A1
- Authority
- US
- United States
- Prior art keywords
- lens
- image quality
- quality signal
- imager
- signal
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Abandoned
Links
Images
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N23/00—Cameras or camera modules comprising electronic image sensors; Control thereof
- H04N23/60—Control of cameras or camera modules
- H04N23/67—Focus control based on electronic image sensor signals
- H04N23/673—Focus control based on electronic image sensor signals based on contrast or high frequency components of image signals, e.g. hill climbing method
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N25/00—Circuitry of solid-state image sensors [SSIS]; Control thereof
- H04N25/60—Noise processing, e.g. detecting, correcting, reducing or removing noise
- H04N25/61—Noise processing, e.g. detecting, correcting, reducing or removing noise the noise originating only from the lens unit, e.g. flare, shading, vignetting or "cos4"
- H04N25/615—Noise processing, e.g. detecting, correcting, reducing or removing noise the noise originating only from the lens unit, e.g. flare, shading, vignetting or "cos4" involving a transfer function modelling the optical system, e.g. optical transfer function [OTF], phase transfer function [PhTF] or modulation transfer function [MTF]
- H04N25/6153—Noise processing, e.g. detecting, correcting, reducing or removing noise the noise originating only from the lens unit, e.g. flare, shading, vignetting or "cos4" involving a transfer function modelling the optical system, e.g. optical transfer function [OTF], phase transfer function [PhTF] or modulation transfer function [MTF] for colour signals
Definitions
- the Auto-focus (AF) lens and process is typically related to the field of digital cameras.
- a lens captures the image of a distant object and focuses the image of the object onto an image plane.
- a conventional auto-focus (AF) lens and process system the system automatically moves the lens and focuses the image on the image plane without the assistance of the operator.
- a conventional AF lens processes information from the image plane electronics and adjusts the position of the lens on the optical axis to maximize the luminance contrast of the image formed on the image plane.
- a closed loop search is conducted to find the best focus position which requires multiple image acquisition. This search takes a significant amount of time to complete.
- the present invention provides direction and distance control signals that allow the lens to be driven to the best focus position with only one image acquisition and without a closed loop search.
- a lens focuses the image of a target object onto an electronic imager(s).
- the initial position of the lens may not be optimal for the given object distance.
- the overall image will be a little blurry at this lens position.
- a first object of the AF lens and process is to provide a Lens Movement. Control System with a positive or negative movement command signal so that the lens is moved initially in the correct direction to obtain a best focus position.
- a second object of this invention is to provide an improved AF process that provides initial directional information to the Lens Movement Control System to move the lens in the direction of best focus without a search.
- Another object of the invention is to provide an improved AF lens and process that provides both the directional information and a distance signal characterizing the distance that the lens has to be moved to obtain proper focus.
- the directional information and the distance signal are coupled to the Lens Movement Control System.
- AF lens and process having a lens that acquires a color image.
- the lens has a predetermined amount of longitudinal chromatic aberrations (LCA).
- LCA of a lens indicates how the focal length of the lens changes with the wavelength of light passing through the lens.
- the lens forms an image of the object on an imager or detector array.
- the imager senses the light on its focal plane and provides an array of pixel intensity values for the primary colors of Red (R), Green (G) and Blue (B).
- the embodiment further comprises a means for calculating R, G and B image quality signals for the respective data arrays of R, G and B.
- the invention auto-focusing lens and system compares the values of the R, G and B image quality signals with each other to determine which is the largest, next to largest and smallest value.
- the invention system uses the amplitude relationships to output a positive direction command to drive the lens in a positive direction if the B image quality signal is greater than the R image quality signal and a negative direction command if the R image quality signal is greater than the B image quality signal.
- an array of lens focus position locations are stored in a memory that corresponds to values of the (R ⁇ B)/G image quality signal difference quotient, where the symbol R, B and G represent the values of R, B and G image quality signals.
- FIG. 1 Is a graph of the modulation transfer function (MTF) at 50 cycle/mm for Red, Green and Blue wavelength entering a lens and being focused on an image plane. The graph is plotted as a function of focus shift.
- MTF modulation transfer function
- FIG. 2 is a functional block diagram of the present invention.
- FIG. 3 a is a logic or process flow chart for the determination of the initial direction of lens movement
- FIG. 3 b is a logic or process flow chart for a continuation of the focus process to fine focus the lens and to test to determine if the option of a DISTANCE ROUTINE should be accessed and performed;
- FIG. 4. is a graph of the ratio of the difference between the values of Red (R) and Blue (B) divided by Red (R) image quality signals and the Focus Shift distance in mm of FIG. 1 where the MTF values at R, G and B are used as the “image quality signals”.
- FIG. 5 is a flow chart for the process and system for determining the distance that the lens has to be moved for proper focus.
- FIG. 6 is a schematic analog circuit for calculating the image quality signal quotient (R ⁇ B)/G.
- the lens system that is used must have a predetermined amount of longitudinal chromatic aberrations (LCA).
- the lens is designed using optical materials that permit tailoring of an LCA; however, it is also possible to add a dispersive optical element to an existing LCA-free lens to obtain the desired or predetermined LCA.
- a dispersive element improves the control of the LCA obtained for a particular lens design.
- FIG. 1 shows the LCA properties of an actual lens design.
- the three curves show the spread in focus shift that is associated with the wavelengths for Red, R, 20 , Green, G, 22 and blue, B, 24 wavelengths or components of light that will be processed into R, G and B image quality signal values.
- the independent variable 26 has the dimension of millimeters of lens movement.
- the dependent variable is an indication of CONTRAST, known as the modulation transfer function (MTF) at 50 lp/mm (line pairs per millimeter) spatial frequency.
- MTF modulation transfer function
- an under-corrected LCA lens the blue component of the spectrum is focused at a point closest to the lens, and the red component of the spectrum is focused at a point farthest from the lens.
- the green component of the spectrum has its focal point approximately half way between the red and blue focal points characterized by the peaks of the curves.
- An example of an under-corrected LCA lens is shown in the graph of FIG. 1.
- the contrast (or Modulation Transfer Function, or MTF at 50 cycle/mm) of a real lens at each R, B and G wavelength is plotted as functions of the shift in focus. The best focus position is located at the point at which the G wavelength MTF is at this peak.
- MTF Modulation Transfer Function
- CFA color filter array
- Commonly used CFA patterns are primary RGB Bayer pattern or the CMY, Complimentary Color patterns.
- three imagers are used, one for each color component with a corresponding color filter in the optical path of each imager.
- the imager 32 or imagers are capable of generating images at three separate R, G and B wavelengths.
- FIG. 2 is a block diagram of an embodiment of the present invention.
- Block 30 represents the lens or objective characterized as having a predetermined LCA.
- an electronic imager 32 consists of an array of photo-detectors (pixels). The imager forms a focal plane for the lens and captures the image of the object. To obtain a color image, the imager 32 , or imager(s) must be capable of acquiring the images at three different wavelengths corresponding to the primary colors of human vision.
- FIG. 2 shows the lens 30 focusing the target image on at least one imager or focal plane array 32 .
- Block 34 represents the process of sampling each of the respective wavelength data from a predetermined region of interest (ROI) on the imager.
- ROI region of interest
- Blocks 36 , 38 and 40 represent the steps of concurrently computing the R, G and B image quality signal values from the Red, R, Green, G and Blue data.
- a DSP digital signal processor
- a DSP digital signal processor
- Block 42 represents the use of an algorithm and/or logic circuit such as that depicted in FIGS. 3 a , 3 b , and FIG. 5 to generate a command to move the lens in a positive direction or in a negative direction.
- block 42 uses the contrast values or R, G and B image quality signal values for each color component in the ROI image, block 42 provides a positive or negative direction command signal.
- FIG. 4 is a derivative of data relating to FIG. 1.
- FIG. 5 will use information from FIG. 4 in an optional process to estimate the amount of lens movement required to be in focus based on the LCA properties of the lens.
- Block 42 represents the algorithms and processes of FIGS. 3 a , 3 b , and 5 which generate direction and distance signals for lens movement.
- the direction and distance signals are coupled to the Lens Movement & Control System represented in FIG. 2 by block 44 .
- Signal lines 84 , 86 couple left and right direction signals and signal line 106 couples a distance signal from block 43 to the Lens Movement & Control System, block 44 which drives the lens in the selected direction in response to the command signals that are received.
- the process begins with a data stream obtained from the imager(s) 32 .
- the data stream is a sequence of intensity values for R, G and B pixels that are read out of the imager 32 and processed as follows.
- a ROI region of interest
- the ROI is a subset of the entire image or frame data array for the image.
- the ROI selected for example, can be the central 10% square area of the entire image. Such a choice might be a built-in design feature.
- the ROI could be made adaptive as a function of the scene to be captured or other parameters.
- the raw image intensity values over the ROI is then segregated into its R, G and B image or pixel intensity data signals.
- Each color component is then further processed, using an elected algorithm, to generate a numeric value that corresponds to R, G and B image quality signals for the ROI.
- the value can be the contrast (MTF) for the image at a specific spatial frequency, or the average contrast over a range of spatial frequencies.
- the value could also be the edge sharpness, or a combination of the edge sharpness and the MTF at the image over a range of spatial frequencies.
- the principal requirement of the elected algorithm is that the numeric values produced for the R, G and B image quality signals must be related to the image quality at wavelengths over the range of interest. Detailed methods for computing image quality signals can be found in books such as “Fundamentals of Electronic Imaging Processing” by Authur R. Weeks, Jr. IEEE press, 1996.
- Region 1 extends from the left vertical axis 18 to the intersection of the B and G curves at 21 .
- Region 2 extends from the intersection of the B and G curves to the intersection of the G and R curves 23 .
- the best focus position is within this region.
- Region 3 extends from the point where G and R curves intersect 23 to the right most limit of the graph.
- the numeric value produced for the R, G and B image quality signals varies with the focus shift in a manner consistent with that shown in FIG. 1.
- the image quality value assigned to R represents the image quality value of the red image.
- the image quality value assigned to the variable B represents the image quality value of the blue image and G represents the image quality value for the green image.
- FIG. 3 a shows a logic process for generating directional signal information that will be coupled to the lens movement control system 44 via signal lines 84 and 86 .
- the process begins at FIG. 3 a at START bubble 48 and is followed with the step of fetching the values of the R, G and B image quality signals per block 50 .
- the process then advances to decision or comparison block 52 to determine if B>R. If the value of B is greater than R, the result is YES and the process advances along path 54 to comparison block 56 to test if B>G. If the answer is YES again, the process uses path 58 to move to block 60 acknowledging that the lens is in region 1 and should be moved in the positive direction toward region 2 .
- the increment of movement is determined by the property of the Lens Movement Control System 44 .
- the lens should continue to move in the positive direction until it reaches region 2 .
- Block 64 by inference, recognizes the implication that if B is not greater than R then R>B. Signal path 66 then leads to decision block 68 to test if R ⁇ G.
- the process uses path 69 to pass to block 71 acknowledging that the lens is in region 3 .
- the process then advances to the next block where drivers are instructed to provide a control or drive signal to block 44 on FIG. 2 to move the lens in a negative direction toward region 2 .
- the increment of movement required is again determined by the property of the lens and the Movement Control System 44 .
- the lens should continue to move in a negative direction until it reaches region 2 .
- the R and B wavelength can be carefully selected so that the R and G signal amplitudes cross at a point where the amplitude of the G signal is at maximum as shown in FIG. 1 at “0” on the axis of the independent variable.
- FIG. 3 b The above algorithm is obtained by the process of FIG. 3 b for fine focusing. If the process is required to obtain a better focus, the process follows the YES signal line from block 72 to the decision block 80 on FIG. 3 b where the question “IS DISTANCE ROUTINE REQUIRED”. Block 80 appears again at the bottom of FIG. 3 b . Block 80 can be positioned at any of several positions on FIGS. 3 a and 3 b . However, if the decision is made at the design or model level, the decision can be replaced with a hard wired YES or NO and the decision block 80 can thereafter be eliminated.
- the process will branch or jump to the beginning of the direction routine at start bubble 48 on the flow chart of FIG. 3 a . If the decision is made to require a DISTANCE ROUTINE, the process will branch or jump to the beginning of the distance routine at start bubble 88 on the flow chart of FIG. 5.
- the combination of the fine focus routine of FIG. 3 b with the distance routing of FIG. 5 provides an improvement upon the current luminance contrast process since it provides fine directional information in combination with distance information to the Lens
- Movement Control System 44 on FIG. 2 will help to reduce the time to find the optimized focus point. It is possible to improve the above logic further by generating not just directional information but also the amount of lens movement required to be at the G peak .
- the Movement Control System 44 adjusts the magnitude of the positive and negative drive signals to a lower level than used initially since the distance that the lens will move is reduced.
- the term “nudge” is used to explain that the magnitude of the impulse or torque command is substantially reduced.
- the ratios of R/G and B/G are calculated.
- the combination of R/G and B/G ratios uniquely determines the absolute position of the lens. From this, it is possible to move the lens to the best focus position in one operation.
- the value of the graph of FIG. 4 is therefore the (R ⁇ B)/G ratio vs. Focus Shift of FIG. 1. This implies that for each (R ⁇ B)/G value, there exists a unique focus shift. So if the (R ⁇ B)/G ratio is calculated, one can deduce the exact amount of focus shift required from the graph of FIG. 4.
- FIG. 4 shows the dependent variable is R/G ⁇ B/G or (R ⁇ B)/G where R, B and G are image quality signals as a function of FOCUS SHIFT distance in mm.
- FIG. 4 uses data from the same lens used for FIG. 1.
- the curve of FIG. 4 is seen to be monotonic over a significant range of distances and particularly over the central portion of the FOCUS SHIFT axis.
- the curve of FIG. 4 is centered at the best focus position.
- the distance and direction that the lens must be moved is read from the curve of FIG. 4 by computing the present value of the difference ratio of (R ⁇ B)/G from the image quality value signals, finding the same value on the independent variable axis of FIG.
- the vertical axis tracing a horizontal line from the value of the image quality signal quotient calculated to intercept the curve of FIG. 4, and then reading the value of the focus shift distance required on the independent variable axis below the intercept point.
- the intercept point 87 is found.
- the intercept point 87 in this example corresponds to a FOCUS SHIFT value of approximately 0.031 mm. Since the value on the independent variable axis is positive, the lens position should be driven in a negative direction through a distance of 0.031 mm to obtain a best focus position.
- the example shows that the exact distance that the lens must be moved or the exact focus shift of the initial lens position as well as the direction that the lens must be moved is available from information on the graph of FIG. 4. It should be understood that the data for the graph of FIG. 4 is related to the design of the lens and can be obtained empirically from a representative lens from a lot or possibly by modeling. Once an array of image quality signal difference quotient data is available for a family of points on the independent variable axis, the array of the (R ⁇ B)/G image quality signal difference quotient values is stored in a memory and used as a look-up table for the FOCUS SHIFT distance for any present value of the (R ⁇ B)/G image quality signal difference quotient from a single or even initial image capture.
- the data relating to the curve of FIG. 4 can be stored in the memory of the processing system either as a look-up table or as a set of coefficients of the best-fit equation. After a present value of the (R ⁇ B)/G image quality signal difference quotient ratio is calculated, a processor can then calculate or look up the exact amount of focus shift in millimeters.
- the distance and direction information is sent to the Lens Movement Control System 44 via signal paths 84 , 86 and 106 to make the appropriate lens adjustment for best focus.
- the curve of FIG. 4 is non-monotonic if the initial position of the lens is too far from the center position of best focus, i.e. where the independent variable has a value of zero.
- the initial lens position or distance from the focal plane or imager is adjusted to be near the center position for best focus.
- the direction only algorithm is initially used to move the lens into its functional range. After the lens is moved into its functional range a second image is generated and used with the distance algorithm to complete the final movement process. Therefore, the initial lens focus calibration need only be set to be within the functional range of the direction algorithm process as the camera is manufactured.
- FIG. 5 is a flow chart of a logic process for determining the amount of focus shift.
- the process begins at the START bubble 88 .
- the process follows the initial steps and functions of FIG. 2 as the process advances to block 90 .
- Block 90 is a step to capture data for a color image frame. The step is performed by the Imager 32 in FIG. 2 and includes the process beginning with the formation of an image on the imager, followed by reading out the pixel and color intensity data for each pixel on the imager. Such imagers are available from companies such as the Sony Corporation.
- the process then advances to block 92 to the step of selecting ROI data from the data for a color image frame which is discussed above.
- the process separates ROI data into R, G and P plane data.
- the process then advances to block 96 where the R, G and B data is used to calculate R, G and B image quality values for the present image.
- the process then advances to block 98 , a measurement circuit as shown in FIG. 6, or preferably a computer process for measuring or calculating a present (R ⁇ B)/G present image quality signal difference quotient for the image on the imager 32 .
- the process then advances to block 100 , a comparator circuit or more preferably a digital process for looking up or finding a best match of the present (R ⁇ B)/G image quality signal difference quotient with those values previously stored in an array of indexed values of (R ⁇ B)/G image quality quotients and corresponding FOCUS SHIFT distance and direction values.
- the comparator circuit outputs or transfers the corresponding lens focus direction and distance via path 106 .
- the Lens Movement Control System 44 to move the lens in the direction and through the specified distance to obtain a best focus position of the lens in the least amount of time and without the steps and time normally associated with a slope chasing servo.
- the comparator circuit of block 100 can be mechanized using a digital processor and a program.
- the function of block 100 is to determine the optimal focus movement value by referring to previously stored pairs of values in a memory array of indexed values of (R ⁇ B)/G image quality signal quotients and corresponding indexed lens focus distances from their respective best focus positions.
- the system With a calculated present value of (R ⁇ B)/G available, and with the present position of the lens known, the system refers to the previously stored look-up array of data and looks up the value of (R ⁇ B)/G image quality quotient that best matches the present value of (R ⁇ B)/G image quality signal difference quotient, The value of lens position corresponding to the best match value of image quality signal difference quotient is then transferred or output at block 104 to the Movement Control System 44 via signal path 106 for movement of the lens in the least amount of time to the best focus position. The process returns to the start bubble for the next iteration via path 108 . In the alternative, the process can return to the start bubble 48 at the top of FIG. 3 a.
- FIG. 7 shows an analog circuit for calculating the image quality signal difference quotients (R ⁇ B)/G.
- the left most operational amplifier is a unity gain inverter.
- the center amplifier sums the ⁇ B term and the R term to provide a result of ⁇ (R ⁇ B).
- the resulting term is inverted by the third amplifier to provide (R ⁇ B) which is then coupled to a first input to a two quadrant analog divider from the Analog Devices company.
- the G term is applied to a buffer amplifier and then pin 3 .
- the (R ⁇ B) term is coupled to pin 1 on the divider.
- the result of (R ⁇ B)/G is obtained at pin 12 . It should be understood that the process performed by the circuit of FIG.
- the signal values would be obtained in digital for for processing using analog to digital converter circuits.
- a sample rate would be established and latch registers would receive sample data at predetermined points in a control process under the control of a digital computer.
- the difference function of (R ⁇ B)/G is used here for illustration purposes only. It is also possible to use other difference functions of R, G and B as long as the difference function varies monotonically with the focus shift distance in a predetermined manner. For example, one could use (R ⁇ circumflex over ( ) ⁇ 2 ⁇ B ⁇ circumflex over ( ) ⁇ 2)/G ⁇ circumflex over ( ) ⁇ 2. The shape of the curve in FIG. 4 will be different. However, the new difference function is still monotonic vs. focus shift and the exact relation between the difference function and the focus shift can be pre-determined.
Abstract
An Auto-focusing Lens system that provides a positive or negative drive command signal and distance information to a Lens Movement Control System to initially move the lens in the correct direction to obtain best focus position without searching. The lens has a pre-determined amount of longitudinal chromatic aberrations (LCA). The lens focuses the image on an imager. The imager senses the light on the focal plane and provides an array of intensity values for the primary colors of Red (R), Green (G) and Blue (B) from a region of interest (ROI) on the imager that are used to calculate R, G and B image quality signals for the R, B and G wavelengths and uses the respective values of the R, B and G image quality signals to determine in which of three regions of focus distance the lens is residing. A look-up table for R, G and B image quality signal difference quotients versus lens position provides a lens movement distance signal.
Description
- This application claims priority from U.S.
Provisional Application 60/447,848 filed Feb. 13, 2003 for an AUTO-FOCUS LENS AND PROCESS IMAGING MODULE and having a common inventor - 1. Field of the Invention
- The Auto-focus (AF) lens and process is typically related to the field of digital cameras.
- 2. Description of Related Art
- In a camera, a lens captures the image of a distant object and focuses the image of the object onto an image plane. In a conventional auto-focus (AF) lens and process system, the system automatically moves the lens and focuses the image on the image plane without the assistance of the operator. A conventional AF lens processes information from the image plane electronics and adjusts the position of the lens on the optical axis to maximize the luminance contrast of the image formed on the image plane.
- The luminance contrast output signal alone, as processed by a conventional auto-focus system, does not provide an initial direction for lens movement nor information on how far the lens must be moved to optimize the luminance contrast signal. A closed loop search is conducted to find the best focus position which requires multiple image acquisition. This search takes a significant amount of time to complete.
- The present invention provides direction and distance control signals that allow the lens to be driven to the best focus position with only one image acquisition and without a closed loop search.
- A lens focuses the image of a target object onto an electronic imager(s). The initial position of the lens may not be optimal for the given object distance. The overall image will be a little blurry at this lens position. A first object of the AF lens and process is to provide a Lens Movement. Control System with a positive or negative movement command signal so that the lens is moved initially in the correct direction to obtain a best focus position.
- A second object of this invention is to provide an improved AF process that provides initial directional information to the Lens Movement Control System to move the lens in the direction of best focus without a search.
- And another object of the invention is to provide an improved AF lens and process that provides both the directional information and a distance signal characterizing the distance that the lens has to be moved to obtain proper focus. The directional information and the distance signal are coupled to the Lens Movement Control System.
- These objects are achieved in a preferred embodiment of the invention AF lens and process having a lens that acquires a color image. The lens has a predetermined amount of longitudinal chromatic aberrations (LCA). The LCA of a lens indicates how the focal length of the lens changes with the wavelength of light passing through the lens. The lens forms an image of the object on an imager or detector array. The imager senses the light on its focal plane and provides an array of pixel intensity values for the primary colors of Red (R), Green (G) and Blue (B). The embodiment further comprises a means for calculating R, G and B image quality signals for the respective data arrays of R, G and B. The invention auto-focusing lens and system compares the values of the R, G and B image quality signals with each other to determine which is the largest, next to largest and smallest value. The invention system then uses the amplitude relationships to output a positive direction command to drive the lens in a positive direction if the B image quality signal is greater than the R image quality signal and a negative direction command if the R image quality signal is greater than the B image quality signal.
- In yet a second embodiment of the AF lens and process, an array of lens focus position locations are stored in a memory that corresponds to values of the (R−B)/G image quality signal difference quotient, where the symbol R, B and G represent the values of R, B and G image quality signals.
- In operation, as a first image is formed on the imager, present values of R, G and B image quality signals are developed. An array of lens focus position locations were previously stored along with corresponding values of the (R−B)/G image quality signal difference quotient for each respective lens focus position location. The process then takes a present value of (R−B)/G and finds the closest match in the array table and uses the corresponding lens focus position location to determine the distance that the lens must be moved from its present location to the new location corresponding to present value image quality signal difference quotient.
- FIG. 1. Is a graph of the modulation transfer function (MTF) at 50 cycle/mm for Red, Green and Blue wavelength entering a lens and being focused on an image plane. The graph is plotted as a function of focus shift.
- FIG. 2 is a functional block diagram of the present invention.
- FIG. 3a is a logic or process flow chart for the determination of the initial direction of lens movement;
- FIG. 3b is a logic or process flow chart for a continuation of the focus process to fine focus the lens and to test to determine if the option of a DISTANCE ROUTINE should be accessed and performed;
- FIG. 4. is a graph of the ratio of the difference between the values of Red (R) and Blue (B) divided by Red (R) image quality signals and the Focus Shift distance in mm of FIG. 1 where the MTF values at R, G and B are used as the “image quality signals”.
- FIG. 5 is a flow chart for the process and system for determining the distance that the lens has to be moved for proper focus.
- FIG. 6 is a schematic analog circuit for calculating the image quality signal quotient (R−B)/G.
- In the present invention, the lens system that is used must have a predetermined amount of longitudinal chromatic aberrations (LCA). The lens is designed using optical materials that permit tailoring of an LCA; however, it is also possible to add a dispersive optical element to an existing LCA-free lens to obtain the desired or predetermined LCA. The use of a dispersive element improves the control of the LCA obtained for a particular lens design.
- FIG. 1 shows the LCA properties of an actual lens design. The three curves show the spread in focus shift that is associated with the wavelengths for Red, R,20, Green, G, 22 and blue, B, 24 wavelengths or components of light that will be processed into R, G and B image quality signal values. The
independent variable 26 has the dimension of millimeters of lens movement. The dependent variable is an indication of CONTRAST, known as the modulation transfer function (MTF) at 50 lp/mm (line pairs per millimeter) spatial frequency. - Longitudinal chromatic aberration is usually considered undesirable for high performance optics. However in this application, we will use the LCA to our advantage to provide the directional signal, and to predict the amount of lens movement required for the lens to be in focus.
- In an under-corrected LCA lens, the blue component of the spectrum is focused at a point closest to the lens, and the red component of the spectrum is focused at a point farthest from the lens. The green component of the spectrum has its focal point approximately half way between the red and blue focal points characterized by the peaks of the curves. An example of an under-corrected LCA lens is shown in the graph of FIG. 1. The contrast (or Modulation Transfer Function, or MTF at 50 cycle/mm) of a real lens at each R, B and G wavelength is plotted as functions of the shift in focus. The best focus position is located at the point at which the G wavelength MTF is at this peak. In an over-corrected LCA lens, the order of wavelength peaks or focal points is reversed.
- In a single imager system, a color filter array (CFA) (not shown) is used in front of the photo-detectors. Commonly used CFA patterns are primary RGB Bayer pattern or the CMY, Complimentary Color patterns. In multiple imager systems, three imagers are used, one for each color component with a corresponding color filter in the optical path of each imager. The
imager 32 or imagers are capable of generating images at three separate R, G and B wavelengths. - FIG. 2 is a block diagram of an embodiment of the present invention.
Block 30 represents the lens or objective characterized as having a predetermined LCA. In a digital imaging system, anelectronic imager 32, consists of an array of photo-detectors (pixels). The imager forms a focal plane for the lens and captures the image of the object. To obtain a color image, theimager 32, or imager(s) must be capable of acquiring the images at three different wavelengths corresponding to the primary colors of human vision. - FIG. 2 shows the
lens 30 focusing the target image on at least one imager orfocal plane array 32.Block 34 represents the process of sampling each of the respective wavelength data from a predetermined region of interest (ROI) on the imager. -
Blocks -
Block 42 represents the use of an algorithm and/or logic circuit such as that depicted in FIGS. 3a, 3 b, and FIG. 5 to generate a command to move the lens in a positive direction or in a negative direction. Using the contrast values or R, G and B image quality signal values for each color component in the ROI image, block 42 provides a positive or negative direction command signal. FIG. 4 is a derivative of data relating to FIG. 1. FIG. 5 will use information from FIG. 4 in an optional process to estimate the amount of lens movement required to be in focus based on the LCA properties of the lens. -
Block 42 represents the algorithms and processes of FIGS. 3a, 3 b, and 5 which generate direction and distance signals for lens movement. The direction and distance signals are coupled to the Lens Movement & Control System represented in FIG. 2 byblock 44.Signal lines signal line 106 couples a distance signal from block 43 to the Lens Movement & Control System, block 44 which drives the lens in the selected direction in response to the command signals that are received. - The process begins with a data stream obtained from the imager(s)32. The data stream is a sequence of intensity values for R, G and B pixels that are read out of the
imager 32 and processed as follows. First, a ROI (region of interest) is selected. The ROI is a subset of the entire image or frame data array for the image. The ROI selected, for example, can be the central 10% square area of the entire image. Such a choice might be a built-in design feature. With added software, the ROI could be made adaptive as a function of the scene to be captured or other parameters. The raw image intensity values over the ROI is then segregated into its R, G and B image or pixel intensity data signals. Each color component is then further processed, using an elected algorithm, to generate a numeric value that corresponds to R, G and B image quality signals for the ROI. - Numerous algorithms or definitions for the R, G and B image quality values are possible. The value can be the contrast (MTF) for the image at a specific spatial frequency, or the average contrast over a range of spatial frequencies. The value could also be the edge sharpness, or a combination of the edge sharpness and the MTF at the image over a range of spatial frequencies. The principal requirement of the elected algorithm is that the numeric values produced for the R, G and B image quality signals must be related to the image quality at wavelengths over the range of interest. Detailed methods for computing image quality signals can be found in books such as “Fundamentals of Electronic Imaging Processing” by Authur R. Weeks, Jr. IEEE press, 1996.
- Brackets at the top of FIG. 1, divided the range of the independent variable into
REGION 1,REGION 2 ANDREGION 3. Region 1: extends from the leftvertical axis 18 to the intersection of the B and G curves at 21. Region 2:extends from the intersection of the B and G curves to the intersection of the G and R curves 23. The best focus position is within this region. Region 3:extends from the point where G and R curves intersect 23 to the right most limit of the graph. The numeric value produced for the R, G and B image quality signals varies with the focus shift in a manner consistent with that shown in FIG. 1. The image quality value assigned to R represents the image quality value of the red image. The image quality value assigned to the variable B represents the image quality value of the blue image and G represents the image quality value for the green image. - FIG. 3a shows a logic process for generating directional signal information that will be coupled to the lens
movement control system 44 viasignal lines START bubble 48 and is followed with the step of fetching the values of the R, G and B image quality signals perblock 50. The process then advances to decision orcomparison block 52 to determine if B>R. If the value of B is greater than R, the result is YES and the process advances alongpath 54 to comparison block 56 to test if B>G. If the answer is YES again, the process usespath 58 to move to block 60 acknowledging that the lens is inregion 1 and should be moved in the positive direction towardregion 2. - The increment of movement is determined by the property of the Lens
Movement Control System 44. The lens should continue to move in the positive direction until it reachesregion 2. - Referring again to FIG. 3a, if the test of
decision block 52 resulted in a NO decision, the result would advance alongpath 62 topath 66 viablock 64.Block 64, by inference, recognizes the implication that if B is not greater than R then R>B. Signal path 66 then leads todecision block 68 to test if R≧G. - If the test of
decision block 68 results in a YES, the process usespath 69 to pass to block 71 acknowledging that the lens is inregion 3. The process then advances to the next block where drivers are instructed to provide a control or drive signal to block 44 on FIG. 2 to move the lens in a negative direction towardregion 2. The increment of movement required is again determined by the property of the lens and theMovement Control System 44. The lens should continue to move in a negative direction until it reachesregion 2. - If the result of the tests of decision blocks56 and 68 were NO, then G>R and G>B and, the lens is in
region 2. The best focus is therefore nearby. Some applications with less demanding requirements or performance requirements permit the AF lens and process to accept the focus position obtained once the lens is inRegion 2 and return to thestart bubble 48. - However, if further AF accuracy is desired, the R and B wavelength can be carefully selected so that the R and G signal amplitudes cross at a point where the amplitude of the G signal is at maximum as shown in FIG. 1 at “0” on the axis of the independent variable. In this case, if B>R the lens
movement control system 44 is commanded to move the lens further in the positive direction until B=R. If R>B, the lensmovement control system 44 is commanded to move the lens further in the negative direction until B=R. Now the lens is at the best focus where G is maximized. - The above algorithm is obtained by the process of FIG. 3b for fine focusing. If the process is required to obtain a better focus, the process follows the YES signal line from
block 72 to thedecision block 80 on FIG. 3b where the question “IS DISTANCE ROUTINE REQUIRED”.Block 80 appears again at the bottom of FIG. 3b.Block 80 can be positioned at any of several positions on FIGS. 3a and 3 b. However, if the decision is made at the design or model level, the decision can be replaced with a hard wired YES or NO and thedecision block 80 can thereafter be eliminated. If the decision is made to not require a DISTANCE ROUTINE, the process will branch or jump to the beginning of the direction routine atstart bubble 48 on the flow chart of FIG. 3a. If the decision is made to require a DISTANCE ROUTINE, the process will branch or jump to the beginning of the distance routine atstart bubble 88 on the flow chart of FIG. 5. The combination of the fine focus routine of FIG. 3b with the distance routing of FIG. 5 provides an improvement upon the current luminance contrast process since it provides fine directional information in combination with distance information to the Lens -
Movement Control System 44 on FIG. 2 will help to reduce the time to find the optimized focus point. It is possible to improve the above logic further by generating not just directional information but also the amount of lens movement required to be at the G peak . Upon executing the fine focus routine of FIG. 3b, theMovement Control System 44 adjusts the magnitude of the positive and negative drive signals to a lower level than used initially since the distance that the lens will move is reduced. The term “nudge” is used to explain that the magnitude of the impulse or torque command is substantially reduced. - Once the focus obtained is inside
region 2, a decision is made to require or not require a fine focus which would require finer lens movement steps leading to a peak in the value of the G image quality signal. Once the lens is inregion 2, it is also possible to use the algorithm of FIG. 5 to calculate the exact distance required to be in the best focus. - As a first step in the fine focus process and fine position process, the ratios of R/G and B/G are calculated. The combination of R/G and B/G ratios uniquely determines the absolute position of the lens. From this, it is possible to move the lens to the best focus position in one operation.
- The value of the graph of FIG. 4 is therefore the (R−B)/G ratio vs. Focus Shift of FIG. 1. This implies that for each (R−B)/G value, there exists a unique focus shift. So if the (R−B)/G ratio is calculated, one can deduce the exact amount of focus shift required from the graph of FIG. 4.
- FIG. 4 shows the dependent variable is R/G−B/G or (R−B)/G where R, B and G are image quality signals as a function of FOCUS SHIFT distance in mm. FIG. 4 uses data from the same lens used for FIG. 1. The curve of FIG. 4 is seen to be monotonic over a significant range of distances and particularly over the central portion of the FOCUS SHIFT axis. The curve of FIG. 4 is centered at the best focus position. The distance and direction that the lens must be moved is read from the curve of FIG. 4 by computing the present value of the difference ratio of (R−B)/G from the image quality value signals, finding the same value on the independent variable axis of FIG. 4, the vertical axis, tracing a horizontal line from the value of the image quality signal quotient calculated to intercept the curve of FIG. 4, and then reading the value of the focus shift distance required on the independent variable axis below the intercept point. Assume that the present value of the (R−B)/G image quality signal difference quotient is 1.0, referring to FIG. 4, the
intercept point 87 is found. Theintercept point 87 in this example corresponds to a FOCUS SHIFT value of approximately 0.031 mm. Since the value on the independent variable axis is positive, the lens position should be driven in a negative direction through a distance of 0.031 mm to obtain a best focus position. - The example shows that the exact distance that the lens must be moved or the exact focus shift of the initial lens position as well as the direction that the lens must be moved is available from information on the graph of FIG. 4. It should be understood that the data for the graph of FIG. 4 is related to the design of the lens and can be obtained empirically from a representative lens from a lot or possibly by modeling. Once an array of image quality signal difference quotient data is available for a family of points on the independent variable axis, the array of the (R−B)/G image quality signal difference quotient values is stored in a memory and used as a look-up table for the FOCUS SHIFT distance for any present value of the (R−B)/G image quality signal difference quotient from a single or even initial image capture. The data relating to the curve of FIG. 4 can be stored in the memory of the processing system either as a look-up table or as a set of coefficients of the best-fit equation. After a present value of the (R−B)/G image quality signal difference quotient ratio is calculated, a processor can then calculate or look up the exact amount of focus shift in millimeters.
- Once available, the distance and direction information is sent to the Lens
Movement Control System 44 viasignal paths - The curve of FIG. 4 is non-monotonic if the initial position of the lens is too far from the center position of best focus, i.e. where the independent variable has a value of zero, For this process to work, the initial lens position or distance from the focal plane or imager is adjusted to be near the center position for best focus.
- In the lens examples discussed in connection with the graphs of FIG. 1 and FIG. 4, if only directional information is desired, at least one of the B or R values must be significantly greater than0. If one wishes to calculate the exact amount of lens movement, the absolute present value of both the R and B image quality values must be >0. The distance process works over a narrower range of focus shift.
- If the initial position of the lens is too great for the distance calculation process to work, the direction only algorithm is initially used to move the lens into its functional range. After the lens is moved into its functional range a second image is generated and used with the distance algorithm to complete the final movement process. Therefore, the initial lens focus calibration need only be set to be within the functional range of the direction algorithm process as the camera is manufactured.
- FIG. 5 is a flow chart of a logic process for determining the amount of focus shift. The process begins at the
START bubble 88. The process follows the initial steps and functions of FIG. 2 as the process advances to block 90.Block 90 is a step to capture data for a color image frame. The step is performed by theImager 32 in FIG. 2 and includes the process beginning with the formation of an image on the imager, followed by reading out the pixel and color intensity data for each pixel on the imager. Such imagers are available from companies such as the Sony Corporation. The process then advances to block 92 to the step of selecting ROI data from the data for a color image frame which is discussed above. In thenext step 94, the process separates ROI data into R, G and P plane data. The process then advances to block 96 where the R, G and B data is used to calculate R, G and B image quality values for the present image. The process then advances to block 98, a measurement circuit as shown in FIG. 6, or preferably a computer process for measuring or calculating a present (R−B)/G present image quality signal difference quotient for the image on theimager 32. - The process then advances to block100, a comparator circuit or more preferably a digital process for looking up or finding a best match of the present (R−B)/G image quality signal difference quotient with those values previously stored in an array of indexed values of (R−B)/G image quality quotients and corresponding FOCUS SHIFT distance and direction values. When the best match is found, the comparator circuit outputs or transfers the corresponding lens focus direction and distance via
path 106. to the LensMovement Control System 44 to move the lens in the direction and through the specified distance to obtain a best focus position of the lens in the least amount of time and without the steps and time normally associated with a slope chasing servo. - The comparator circuit of
block 100, can be mechanized using a digital processor and a program. The function ofblock 100 is to determine the optimal focus movement value by referring to previously stored pairs of values in a memory array of indexed values of (R−B)/G image quality signal quotients and corresponding indexed lens focus distances from their respective best focus positions. With a calculated present value of (R−B)/G available, and with the present position of the lens known, the system refers to the previously stored look-up array of data and looks up the value of (R−B)/G image quality quotient that best matches the present value of (R−B)/G image quality signal difference quotient, The value of lens position corresponding to the best match value of image quality signal difference quotient is then transferred or output atblock 104 to theMovement Control System 44 viasignal path 106 for movement of the lens in the least amount of time to the best focus position. The process returns to the start bubble for the next iteration viapath 108. In the alternative, the process can return to thestart bubble 48 at the top of FIG. 3a. - FIG. 7 shows an analog circuit for calculating the image quality signal difference quotients (R−B)/G. As shown the left most operational amplifier is a unity gain inverter. The center amplifier sums the −B term and the R term to provide a result of −(R−B). The resulting term is inverted by the third amplifier to provide (R−B) which is then coupled to a first input to a two quadrant analog divider from the Analog Devices company. The G term is applied to a buffer amplifier and then pin3. The (R−B) term is coupled to pin 1 on the divider. The result of (R−B)/G is obtained at
pin 12. It should be understood that the process performed by the circuit of FIG. 7 could be performed by a digital computer running a program or routine. The signal values would be obtained in digital for for processing using analog to digital converter circuits. A sample rate would be established and latch registers would receive sample data at predetermined points in a control process under the control of a digital computer. - The difference function of (R−B)/G is used here for illustration purposes only. It is also possible to use other difference functions of R, G and B as long as the difference function varies monotonically with the focus shift distance in a predetermined manner. For example, one could use (R{circumflex over ( )}2−B{circumflex over ( )}2)/G{circumflex over ( )}2. The shape of the curve in FIG. 4 will be different. However, the new difference function is still monotonic vs. focus shift and the exact relation between the difference function and the focus shift can be pre-determined.
- Those skilled in the art will appreciate that various adaptations and modifications of the preferred embodiments can be configured without departing from the scope and spirit of the invention. It is to be understood that the invention may be practiced other than as specifically described herein, within the scope of the appended claims.
Claims (13)
1. An AF lens and process system for movement of a lens, while acquiring a color image, to obtain the best focus of the color image on the focal plane of an imager comprising:
a lens having a pre-determined amount of longitudinal chromatic aberrations (LCA), the lens focusing the color image on the focal plane of an imager, the imager providing an array of intensity values for the primary colors of Red (R). Green (G) and Blue (B),
a means responsive to the intensity values of each of the primary colors for calculating an R, G and B image quality signal, a means for comparing the values of the R, G and B image quality signals and,
a logic circuit for outputting a positive direction command to drive the lens in a positive direction if the B image quality signal is greater than the R image quality signal and to output a negative direction command to drive the lens in the opposite direction if the R image quality signal is greater than the B image quality signal.
2. The AF lens and process of claim 1 wherein the logic circuit for outputting a positive direction command signal or a negative direction command signal is further characterized to start executing a fine focus distance routine process if the G image quality signal is greater than the R image quality signal and if the G image quality signal is greater than the B image quality signal, the fine focus distance routine process providing a nudge drive signal to a Lens Movement Control System to moving the lens in a direction characterized to drive the lens to a position at which the R image quality signal substantially equals the B image quality signal.
3. An AF lens and process system for movement of a lens, while acquiring a color image, to obtain the best focus of the color image on the focal plane of an imager comprising:
providing a lens having a pre-determined amount of longitudinal chromatic aberrations (LCA), the lens focusing the color image on the focal plane of an imager, the imager providing an array of data values for the primary colors of Red (R), Green (G) and Blue (B),
providing a means for calculating an R, G and B image quality signal, and for calculating a present difference function based on the R, G and B image quality signals,
providing a means for comparing the present calculated difference function with previously stored calculated difference functions in an array of previously stored calculated difference functions with corresponding lens position distance value pairs and
finding a best match between the present calculated difference function and the previously stored calculated difference function, and outputting the corresponding lens position distance value to
a Lens Movement Control System for moving the lens to obtain a best focus position.
4. The AF lens and process system of claim 3 wherein the calculated difference function further comprises:
a process for subtracting the value of B from R and dividing the difference by the value of G.
5. The AF lens and process system of claim 3 wherein the calculated difference function further comprises:
a process for subtracting the value of the square of B from the square of R and dividing the difference by the value of the square of G.
6. An AF lens and process system for movement of a lens, while acquiring a color image, to obtain the best focus of the color image on the focal plane of an imager comprising:
a lens having a pre-determined amount of longitudinal chromatic aberrations (LCA) through an aperture, the lens focusing the color image on the focal plane of an imager, the imager providing an array of intensity values for the primary colors of Red (R). Green (G) and Blue (B),
a means responsive to the intensity values of each of the primary colors for calculating an R, G and B image quality signal,
a comparator circuit for comparing the values of the R, G and B image quality signals and,
a logic circuit for outputting a positive direction command to drive the lens in a first direction if the B image quality signal is greater than the R image quality signal and if the R image quality signal is greater than the G image quality signal.
7. The AF lens and process of claim 6 wherein the logic circuit for outputting a positive direction command is further characterized to output a negative direction command to drive the lens in a second direction if the R image quality signal is greater than the B image quality signal and if the R image quality signal is greater than the G image quality signal.
8. The AF lens and process of claim 6 further comprising:
an array of indexed values of (R−B)/G image quality signal quotients and corresponding indexed respective lens focus distances from a best focus position,
a measurement circuit for calculating the present value of the (R−B)/G image quality signal quotient for a present image, and
a comparator circuit for finding a best match of the present value of the (R−B)/G quotient with the indexed values of (R−B)/G quotients and transferring its indexed respective lens focus distances from a best focus position to
a Lens Movement Control System for moving the lens through a distance corresponding to the indexed respective lens focus distance to obtain a best focus position in the least amount of time.
9. The AF lens and process of claim 7 wherein the logic circuit for outputting a positive direction command signal or a negative direction command signal is further characterized to start executing a fine focus distance routine process if the G image quality signal is greater than the R image quality signal and if the G image quality signal is greater than the B image quality signal, the fine focus distance routine process providing a nudge drive signal to
a Lens Movement Control System to moving the lens in a direction characterized to drive the lens to a position at which the R image quality signal equals the B image quality signal.
10. The AF lens and process of claim 9 wherein the fine focus distance routine process further comprises the steps determining if the B image quality signal is equal to the R image quality signal, if the answer is YES, the process determines that the lens is IN FOCUS and the process optionally advances to the decision block to test to determine IS DISTANCE ROUTINE REQUIRED, if the answer is NO, the process advances to the step to determining if the R image quality signal is greater than the B image quality signal and if the answer is YES, the process nudges the lens in POSITIVE direction and the process optionally advances to the decision block to test to determine IS DISTANCE ROUTINE REQUIRED, if the process advances to the step to determine if the R image quality signal is greater than the B image quality signal and the answer is NO, the process advances to the step to determining if the B image quality signal is greater than the R image quality signal and if the answer is YES, the process nudges the lens in NEGITATIVE direction and the process optionally advances to the decision block to test to determine IS DISTANCE ROUTINE REQUIRED.
11. An AF lens and process system for movement of a lens, while acquiring a color image, to obtain the best focus of the color image on the focal plane of an imager comprising:
providing a lens having a pre-determined amount of longitudinal chromatic aberrations (LCA), the lens focusing the color image on the focal plane of an imager, the imager providing an array of data values for the primary colors of Red (R), Green (G) and Blue (B),
providing a means for calculating an R, G and B image quality signal, and for calculating a present difference function based on the R, G and B image quality signals,
providing a means for comparing the present calculated difference function with previously stored calculated difference functions in an array of previously stored calculated difference functions with corresponding lens position distance value pairs and
finding a best match between the present calculated difference function and the previously stored calculated difference function, and outputting the corresponding lens position distance value to
a Lens Movement Control System for moving the lens to obtain a best focus position.
12. The AF lens and process system of claim 11 wherein the calculated difference function further comprises:
a process for subtracting the value of B from R and dividing the difference by the value of G.
13. The AF lens and process system of claim 11 wherein the calculated difference function further comprises:
a process for subtracting the value of the square of B from the square of R and dividing the difference by the value of the square of G.
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US10/778,785 US20040165090A1 (en) | 2003-02-13 | 2004-02-13 | Auto-focus (AF) lens and process |
Applications Claiming Priority (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US44784803P | 2003-02-13 | 2003-02-13 | |
US10/778,785 US20040165090A1 (en) | 2003-02-13 | 2004-02-13 | Auto-focus (AF) lens and process |
Publications (1)
Publication Number | Publication Date |
---|---|
US20040165090A1 true US20040165090A1 (en) | 2004-08-26 |
Family
ID=32872045
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
US10/778,785 Abandoned US20040165090A1 (en) | 2003-02-13 | 2004-02-13 | Auto-focus (AF) lens and process |
Country Status (1)
Country | Link |
---|---|
US (1) | US20040165090A1 (en) |
Cited By (141)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
WO2006095110A2 (en) * | 2005-03-07 | 2006-09-14 | Dxo Labs | Method of controlling an action, such as a sharpness modification, using a colour digital image |
EP1949671A1 (en) * | 2005-11-15 | 2008-07-30 | Nokia Corporation | Imaging system with adjustable optics |
US20080225410A1 (en) * | 2006-05-08 | 2008-09-18 | Boston Scientific Scimed, Inc. | Optical assembly for medical imaging devices |
US20080297649A1 (en) * | 2007-05-31 | 2008-12-04 | Igor Subbotin | Methods and apparatus providing light assisted automatic focus |
US20090142046A1 (en) * | 2007-12-04 | 2009-06-04 | Hon Hai Precision Industry Co., Ltd. | Focusing system and method for enhancing resolution of an optical system |
WO2009117678A1 (en) * | 2008-03-21 | 2009-09-24 | Abbott Point Of Care, Inc. | Method and apparatus for determining a focal position of an imaging device adapted to image a biologic sample |
US20090239257A1 (en) * | 2008-03-21 | 2009-09-24 | Abbott Point Of Care, Inc. | Method and apparatus for analyzing individual cells or particulates using fluorescent quenching and/or bleaching |
US20090238438A1 (en) * | 2008-03-21 | 2009-09-24 | Abbott Point Of Care, Inc. | Method and apparatus for determining red blood cell indices of a blood sample utilizing the intrinsic pigmentation of hemoglobin contained within the red blood cells |
US20090238439A1 (en) * | 2008-03-21 | 2009-09-24 | Abbott Point Of Care, Inc. | Method and apparatus for detecting and counting platelets individually and in aggregate clumps |
US20090238437A1 (en) * | 2008-03-21 | 2009-09-24 | Abbott Point Of Care, Inc. | Method and apparatus for determining the hematocrit of a blood sample utilizing the intrinsic pigmentation of hemoglobin contained within the red blood cells |
US20090252399A1 (en) * | 2008-04-02 | 2009-10-08 | Abbott Point Of Care, Inc. | Self-calibrating gradient dilution in a constituent assay and gradient dilution apparatus performed in a thin film sample |
US20090258371A1 (en) * | 2008-04-09 | 2009-10-15 | Abbott Point Of Care, Inc. | Method of detecting very low levels of analyte within a thin film fluid sample contained in a thin thickness chamber |
US20090257632A1 (en) * | 2008-04-09 | 2009-10-15 | Abbott Point Of Care, Inc. | Method for measuring the area of a sample disposed within an analysis chamber |
US20100255605A1 (en) * | 2009-04-02 | 2010-10-07 | Abbott Point Of Care, Inc. | Method and device for transferring biologic fluid samples |
US20110096175A1 (en) * | 2009-10-27 | 2011-04-28 | Hon Hai Precision Industry Co., Ltd. | Method for adjusting a focal length of a camera |
US20110164803A1 (en) * | 2009-12-31 | 2011-07-07 | Abbott Point Of Care, Inc. | Method and apparatus for determining mean cell volume of red blood cells |
CN102129146A (en) * | 2010-01-12 | 2011-07-20 | 株式会社尼康 | Image pickup device |
US20110261208A1 (en) * | 2010-04-27 | 2011-10-27 | Ability Enterprise Co., Ltd. | Method of automatically calibrating a visual parameter for an imaging device |
US20120051730A1 (en) * | 2010-09-01 | 2012-03-01 | Apple Inc. | Auto-focus control using image statistics data with coarse and fine auto-focus scores |
US20120307089A1 (en) * | 2011-05-31 | 2012-12-06 | Apple Inc. | Estimating optical characteristics of a camera component using sharpness sweep data |
US20130050546A1 (en) * | 2011-08-30 | 2013-02-28 | Canon Kabushiki Kaisha | Image processing apparatus and method |
US20130057753A1 (en) * | 2011-03-28 | 2013-03-07 | Intermec Ip Corp. | Two-dimensional imager with solid-state auto-focus |
US20130113889A1 (en) * | 2011-11-09 | 2013-05-09 | Hon Hai Precision Industry Co., Ltd. | Stereo image capturing device |
US8472693B2 (en) | 2010-03-18 | 2013-06-25 | Abbott Point Of Care, Inc. | Method for determining at least one hemoglobin related parameter of a whole blood sample |
US8619182B2 (en) | 2012-03-06 | 2013-12-31 | Csr Technology Inc. | Fast auto focus techniques for digital cameras |
US20140002674A1 (en) * | 2012-06-30 | 2014-01-02 | Pelican Imaging Corporation | Systems and Methods for Manufacturing Camera Modules Using Active Alignment of Lens Stack Arrays and Sensors |
US8817120B2 (en) | 2012-05-31 | 2014-08-26 | Apple Inc. | Systems and methods for collecting fixed pattern noise statistics of image data |
US8831367B2 (en) | 2011-09-28 | 2014-09-09 | Pelican Imaging Corporation | Systems and methods for decoding light field image files |
US8861089B2 (en) | 2009-11-20 | 2014-10-14 | Pelican Imaging Corporation | Capturing and processing of images using monolithic camera array with heterogeneous imagers |
US8866912B2 (en) | 2013-03-10 | 2014-10-21 | Pelican Imaging Corporation | System and methods for calibration of an array camera using a single captured image |
US8866920B2 (en) | 2008-05-20 | 2014-10-21 | Pelican Imaging Corporation | Capturing and processing of images using monolithic camera array with heterogeneous imagers |
US8872946B2 (en) | 2012-05-31 | 2014-10-28 | Apple Inc. | Systems and methods for raw image processing |
US8878950B2 (en) | 2010-12-14 | 2014-11-04 | Pelican Imaging Corporation | Systems and methods for synthesizing high resolution images using super-resolution processes |
US8885059B1 (en) | 2008-05-20 | 2014-11-11 | Pelican Imaging Corporation | Systems and methods for measuring depth using images captured by camera arrays |
US8917336B2 (en) | 2012-05-31 | 2014-12-23 | Apple Inc. | Image signal processing involving geometric distortion correction |
US8928793B2 (en) | 2010-05-12 | 2015-01-06 | Pelican Imaging Corporation | Imager array interfaces |
US20150009394A1 (en) * | 2012-01-17 | 2015-01-08 | Nokia Corporation | Focusing control method using colour channel analysis |
US8953882B2 (en) | 2012-05-31 | 2015-02-10 | Apple Inc. | Systems and methods for determining noise statistics of image data |
US9007368B2 (en) | 2012-05-07 | 2015-04-14 | Intermec Ip Corp. | Dimensioning system calibration systems and methods |
US9014504B2 (en) | 2012-05-31 | 2015-04-21 | Apple Inc. | Systems and methods for highlight recovery in an image signal processor |
US9025867B2 (en) | 2012-05-31 | 2015-05-05 | Apple Inc. | Systems and methods for YCC image processing |
US9031319B2 (en) | 2012-05-31 | 2015-05-12 | Apple Inc. | Systems and methods for luma sharpening |
US9077943B2 (en) | 2012-05-31 | 2015-07-07 | Apple Inc. | Local image statistics collection |
US9080856B2 (en) | 2013-03-13 | 2015-07-14 | Intermec Ip Corp. | Systems and methods for enhancing dimensioning, for example volume dimensioning |
US9083885B1 (en) * | 2013-11-18 | 2015-07-14 | Google Inc. | Fixed focus camera module with near-field image recognition |
US9100635B2 (en) | 2012-06-28 | 2015-08-04 | Pelican Imaging Corporation | Systems and methods for detecting defective camera arrays and optic arrays |
US9100586B2 (en) | 2013-03-14 | 2015-08-04 | Pelican Imaging Corporation | Systems and methods for photometric normalization in array cameras |
US9106784B2 (en) | 2013-03-13 | 2015-08-11 | Pelican Imaging Corporation | Systems and methods for controlling aliasing in images captured by an array camera for use in super-resolution processing |
US9105078B2 (en) | 2012-05-31 | 2015-08-11 | Apple Inc. | Systems and methods for local tone mapping |
US9124831B2 (en) | 2013-03-13 | 2015-09-01 | Pelican Imaging Corporation | System and methods for calibration of an array camera |
US9123118B2 (en) | 2012-08-21 | 2015-09-01 | Pelican Imaging Corporation | System and methods for measuring depth using an array camera employing a bayer filter |
US9128228B2 (en) | 2011-06-28 | 2015-09-08 | Pelican Imaging Corporation | Optical arrangements for use with an array camera |
US9131196B2 (en) | 2012-05-31 | 2015-09-08 | Apple Inc. | Systems and methods for defective pixel correction with neighboring pixels |
US9143711B2 (en) | 2012-11-13 | 2015-09-22 | Pelican Imaging Corporation | Systems and methods for array camera focal plane control |
US9142012B2 (en) | 2012-05-31 | 2015-09-22 | Apple Inc. | Systems and methods for chroma noise reduction |
US9185276B2 (en) | 2013-11-07 | 2015-11-10 | Pelican Imaging Corporation | Methods of manufacturing array camera modules incorporating independently aligned lens stacks |
US9197821B2 (en) | 2011-05-11 | 2015-11-24 | Pelican Imaging Corporation | Systems and methods for transmitting and receiving array camera image data |
US9210392B2 (en) | 2012-05-01 | 2015-12-08 | Pelican Imaging Coporation | Camera modules patterned with pi filter groups |
US9214013B2 (en) | 2012-09-14 | 2015-12-15 | Pelican Imaging Corporation | Systems and methods for correcting user identified artifacts in light field images |
US9239950B2 (en) | 2013-07-01 | 2016-01-19 | Hand Held Products, Inc. | Dimensioning system |
US9247117B2 (en) | 2014-04-07 | 2016-01-26 | Pelican Imaging Corporation | Systems and methods for correcting for warpage of a sensor array in an array camera module by introducing warpage into a focal plane of a lens stack array |
US9253380B2 (en) | 2013-02-24 | 2016-02-02 | Pelican Imaging Corporation | Thin form factor computational array cameras and modular array cameras |
US9332239B2 (en) | 2012-05-31 | 2016-05-03 | Apple Inc. | Systems and methods for RGB image processing |
US9392159B2 (en) | 2011-09-02 | 2016-07-12 | Nikon Corporation | Focus estimating device, imaging device, and storage medium storing image processing program |
US9412206B2 (en) | 2012-02-21 | 2016-08-09 | Pelican Imaging Corporation | Systems and methods for the manipulation of captured light field image data |
US9426361B2 (en) | 2013-11-26 | 2016-08-23 | Pelican Imaging Corporation | Array camera configurations incorporating multiple constituent array cameras |
US9438888B2 (en) | 2013-03-15 | 2016-09-06 | Pelican Imaging Corporation | Systems and methods for stereo imaging with camera arrays |
US9445003B1 (en) | 2013-03-15 | 2016-09-13 | Pelican Imaging Corporation | Systems and methods for synthesizing high resolution images using image deconvolution based on motion and depth information |
US9462164B2 (en) | 2013-02-21 | 2016-10-04 | Pelican Imaging Corporation | Systems and methods for generating compressed light field representation data using captured light fields, array geometry, and parallax information |
US9464885B2 (en) | 2013-08-30 | 2016-10-11 | Hand Held Products, Inc. | System and method for package dimensioning |
US9497370B2 (en) | 2013-03-15 | 2016-11-15 | Pelican Imaging Corporation | Array camera architecture implementing quantum dot color filters |
US9497429B2 (en) | 2013-03-15 | 2016-11-15 | Pelican Imaging Corporation | Extended color processing on pelican array cameras |
US9516222B2 (en) | 2011-06-28 | 2016-12-06 | Kip Peli P1 Lp | Array cameras incorporating monolithic array camera modules with high MTF lens stacks for capture of images used in super-resolution processing |
US9521416B1 (en) | 2013-03-11 | 2016-12-13 | Kip Peli P1 Lp | Systems and methods for image data compression |
US9521319B2 (en) | 2014-06-18 | 2016-12-13 | Pelican Imaging Corporation | Array cameras and array camera modules including spectral filters disposed outside of a constituent image sensor |
US9519972B2 (en) | 2013-03-13 | 2016-12-13 | Kip Peli P1 Lp | Systems and methods for synthesizing images from image data captured by an array camera using restricted depth of field depth maps in which depth estimation precision varies |
US9557166B2 (en) | 2014-10-21 | 2017-01-31 | Hand Held Products, Inc. | Dimensioning system with multipath interference mitigation |
US9578259B2 (en) | 2013-03-14 | 2017-02-21 | Fotonation Cayman Limited | Systems and methods for reducing motion blur in images or video in ultra low light with array cameras |
US9633442B2 (en) | 2013-03-15 | 2017-04-25 | Fotonation Cayman Limited | Array cameras including an array camera module augmented with a separate camera |
US9638883B1 (en) | 2013-03-04 | 2017-05-02 | Fotonation Cayman Limited | Passive alignment of array camera modules constructed from lens stack arrays and sensors based upon alignment information obtained during manufacture of array camera modules using an active alignment process |
US9639947B2 (en) | 2012-04-18 | 2017-05-02 | Sony Corporation | Method and optical system for determining a depth map of an image |
US9752864B2 (en) | 2014-10-21 | 2017-09-05 | Hand Held Products, Inc. | Handheld dimensioning system with feedback |
US9762793B2 (en) | 2014-10-21 | 2017-09-12 | Hand Held Products, Inc. | System and method for dimensioning |
US9774789B2 (en) | 2013-03-08 | 2017-09-26 | Fotonation Cayman Limited | Systems and methods for high dynamic range imaging using array cameras |
US9779546B2 (en) | 2012-05-04 | 2017-10-03 | Intermec Ip Corp. | Volume dimensioning systems and methods |
US9779276B2 (en) | 2014-10-10 | 2017-10-03 | Hand Held Products, Inc. | Depth sensor based auto-focus system for an indicia scanner |
US9786101B2 (en) | 2015-05-19 | 2017-10-10 | Hand Held Products, Inc. | Evaluating image values |
US9794476B2 (en) | 2011-09-19 | 2017-10-17 | Fotonation Cayman Limited | Systems and methods for controlling aliasing in images captured by an array camera for use in super resolution processing using pixel apertures |
US9813616B2 (en) | 2012-08-23 | 2017-11-07 | Fotonation Cayman Limited | Feature based high resolution motion estimation from low resolution images captured using an array source |
US9823059B2 (en) | 2014-08-06 | 2017-11-21 | Hand Held Products, Inc. | Dimensioning system with guided alignment |
US9835486B2 (en) | 2015-07-07 | 2017-12-05 | Hand Held Products, Inc. | Mobile dimensioner apparatus for use in commerce |
US9841311B2 (en) | 2012-10-16 | 2017-12-12 | Hand Held Products, Inc. | Dimensioning system |
US9857167B2 (en) | 2015-06-23 | 2018-01-02 | Hand Held Products, Inc. | Dual-projector three-dimensional scanner |
US9888194B2 (en) | 2013-03-13 | 2018-02-06 | Fotonation Cayman Limited | Array camera architecture implementing quantum film image sensors |
US9897434B2 (en) | 2014-10-21 | 2018-02-20 | Hand Held Products, Inc. | Handheld dimensioning system with measurement-conformance feedback |
US9898856B2 (en) | 2013-09-27 | 2018-02-20 | Fotonation Cayman Limited | Systems and methods for depth-assisted perspective distortion correction |
US9940721B2 (en) | 2016-06-10 | 2018-04-10 | Hand Held Products, Inc. | Scene change detection in a dimensioner |
US9939259B2 (en) | 2012-10-04 | 2018-04-10 | Hand Held Products, Inc. | Measuring object dimensions using mobile computer |
US9942474B2 (en) | 2015-04-17 | 2018-04-10 | Fotonation Cayman Limited | Systems and methods for performing high speed video capture and depth estimation using array cameras |
US10007858B2 (en) | 2012-05-15 | 2018-06-26 | Honeywell International Inc. | Terminals and methods for dimensioning objects |
US10025314B2 (en) | 2016-01-27 | 2018-07-17 | Hand Held Products, Inc. | Vehicle positioning and object avoidance |
US10060729B2 (en) | 2014-10-21 | 2018-08-28 | Hand Held Products, Inc. | Handheld dimensioner with data-quality indication |
US10066982B2 (en) | 2015-06-16 | 2018-09-04 | Hand Held Products, Inc. | Calibrating a volume dimensioner |
US10089740B2 (en) | 2014-03-07 | 2018-10-02 | Fotonation Limited | System and methods for depth regularization and semiautomatic interactive matting using RGB-D images |
US10094650B2 (en) | 2015-07-16 | 2018-10-09 | Hand Held Products, Inc. | Dimensioning and imaging items |
EP3385686A1 (en) * | 2017-04-04 | 2018-10-10 | Hand Held Products, Inc. | Multi-spectral imaging using longitudinal chromatic aberrations |
US10122993B2 (en) | 2013-03-15 | 2018-11-06 | Fotonation Limited | Autofocus system for a conventional camera that uses depth information from an array camera |
US10119808B2 (en) | 2013-11-18 | 2018-11-06 | Fotonation Limited | Systems and methods for estimating depth from projected texture using camera arrays |
US10134120B2 (en) | 2014-10-10 | 2018-11-20 | Hand Held Products, Inc. | Image-stitching for dimensioning |
US10140724B2 (en) | 2009-01-12 | 2018-11-27 | Intermec Ip Corporation | Semi-automatic dimensioning with imager on a portable device |
US10163216B2 (en) | 2016-06-15 | 2018-12-25 | Hand Held Products, Inc. | Automatic mode switching in a volume dimensioner |
US10203402B2 (en) | 2013-06-07 | 2019-02-12 | Hand Held Products, Inc. | Method of error correction for 3D imaging device |
US10225544B2 (en) | 2015-11-19 | 2019-03-05 | Hand Held Products, Inc. | High resolution dot pattern |
US10249030B2 (en) | 2015-10-30 | 2019-04-02 | Hand Held Products, Inc. | Image transformation for indicia reading |
US10250871B2 (en) | 2014-09-29 | 2019-04-02 | Fotonation Limited | Systems and methods for dynamic calibration of array cameras |
US10247547B2 (en) | 2015-06-23 | 2019-04-02 | Hand Held Products, Inc. | Optical pattern projector |
US10321127B2 (en) | 2012-08-20 | 2019-06-11 | Intermec Ip Corp. | Volume dimensioning system calibration systems and methods |
US10339352B2 (en) | 2016-06-03 | 2019-07-02 | Hand Held Products, Inc. | Wearable metrological apparatus |
US10390005B2 (en) | 2012-09-28 | 2019-08-20 | Fotonation Limited | Generating images from light fields utilizing virtual viewpoints |
US10393506B2 (en) | 2015-07-15 | 2019-08-27 | Hand Held Products, Inc. | Method for a mobile dimensioning device to use a dynamic accuracy compatible with NIST standard |
WO2019182357A1 (en) * | 2018-03-21 | 2019-09-26 | Samsung Electronics Co., Ltd. | Method for adjusting focus based on spread-level of display object and electronic device supporting the same |
US20190323888A1 (en) * | 2018-04-18 | 2019-10-24 | Raytheon Company | Spectrally-scanned hyperspectral electro-optical sensor for instantaneous situational awareness |
US10482618B2 (en) | 2017-08-21 | 2019-11-19 | Fotonation Limited | Systems and methods for hybrid depth regularization |
US10584962B2 (en) | 2018-05-01 | 2020-03-10 | Hand Held Products, Inc | System and method for validating physical-item security |
US10733748B2 (en) | 2017-07-24 | 2020-08-04 | Hand Held Products, Inc. | Dual-pattern optical 3D dimensioning |
US10775165B2 (en) | 2014-10-10 | 2020-09-15 | Hand Held Products, Inc. | Methods for improving the accuracy of dimensioning-system measurements |
US10909708B2 (en) | 2016-12-09 | 2021-02-02 | Hand Held Products, Inc. | Calibrating a dimensioner using ratios of measurable parameters of optic ally-perceptible geometric elements |
US11029762B2 (en) | 2015-07-16 | 2021-06-08 | Hand Held Products, Inc. | Adjusting dimensioning results using augmented reality |
US20210192706A1 (en) * | 2019-12-19 | 2021-06-24 | Acer Incorporated | Lens matching apparatus and lens matching method |
US11047672B2 (en) | 2017-03-28 | 2021-06-29 | Hand Held Products, Inc. | System for optically dimensioning |
US11089247B2 (en) | 2012-05-31 | 2021-08-10 | Apple Inc. | Systems and method for reducing fixed pattern noise in image data |
US11270110B2 (en) | 2019-09-17 | 2022-03-08 | Boston Polarimetrics, Inc. | Systems and methods for surface modeling using polarization cues |
US11290658B1 (en) | 2021-04-15 | 2022-03-29 | Boston Polarimetrics, Inc. | Systems and methods for camera exposure control |
US11302012B2 (en) | 2019-11-30 | 2022-04-12 | Boston Polarimetrics, Inc. | Systems and methods for transparent object segmentation using polarization cues |
US11402635B1 (en) * | 2018-05-24 | 2022-08-02 | Facebook Technologies, Llc | Systems and methods for measuring visual refractive error |
US11525906B2 (en) | 2019-10-07 | 2022-12-13 | Intrinsic Innovation Llc | Systems and methods for augmentation of sensor systems and imaging systems with polarization |
US11580667B2 (en) | 2020-01-29 | 2023-02-14 | Intrinsic Innovation Llc | Systems and methods for characterizing object pose detection and measurement systems |
US11639846B2 (en) | 2019-09-27 | 2023-05-02 | Honeywell International Inc. | Dual-pattern optical 3D dimensioning |
US11689813B2 (en) | 2021-07-01 | 2023-06-27 | Intrinsic Innovation Llc | Systems and methods for high dynamic range imaging using crossed polarizers |
US11792538B2 (en) | 2008-05-20 | 2023-10-17 | Adeia Imaging Llc | Capturing and processing of images including occlusions focused on an image sensor by a lens stack array |
US11797863B2 (en) | 2020-01-30 | 2023-10-24 | Intrinsic Innovation Llc | Systems and methods for synthesizing data for training statistical models on different imaging modalities including polarized images |
Citations (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US5973846A (en) * | 1998-11-30 | 1999-10-26 | Hewlett-Packard Company | Offset spectra lens system for a two spectra automatic focusing system |
US6363220B1 (en) * | 1999-03-16 | 2002-03-26 | Olympus Optical Co., Ltd. | Camera and autofocus apparatus |
US6710806B1 (en) * | 1999-08-19 | 2004-03-23 | Ricoh Company, Ltd. | Automatically focusing system and focusing method therefor |
US6930720B2 (en) * | 1995-09-20 | 2005-08-16 | Canon Kabushiki Kaisha | Video camera system with interchangeable lens assembly |
US6963388B1 (en) * | 1999-08-31 | 2005-11-08 | Canon Kabushiki Kaisha | Focus adjustment apparatus, control method therefor, and medium for supplying control program therefor |
US7057659B1 (en) * | 1999-07-08 | 2006-06-06 | Olympus Corporation | Image pickup device and image pickup optical system |
-
2004
- 2004-02-13 US US10/778,785 patent/US20040165090A1/en not_active Abandoned
Patent Citations (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US6930720B2 (en) * | 1995-09-20 | 2005-08-16 | Canon Kabushiki Kaisha | Video camera system with interchangeable lens assembly |
US5973846A (en) * | 1998-11-30 | 1999-10-26 | Hewlett-Packard Company | Offset spectra lens system for a two spectra automatic focusing system |
US6363220B1 (en) * | 1999-03-16 | 2002-03-26 | Olympus Optical Co., Ltd. | Camera and autofocus apparatus |
US7057659B1 (en) * | 1999-07-08 | 2006-06-06 | Olympus Corporation | Image pickup device and image pickup optical system |
US6710806B1 (en) * | 1999-08-19 | 2004-03-23 | Ricoh Company, Ltd. | Automatically focusing system and focusing method therefor |
US6963388B1 (en) * | 1999-08-31 | 2005-11-08 | Canon Kabushiki Kaisha | Focus adjustment apparatus, control method therefor, and medium for supplying control program therefor |
Cited By (356)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN102984448A (en) * | 2005-03-07 | 2013-03-20 | 德克索实验室 | Method of controlling an action, such as a sharpness modification, using a colour digital image |
US20080158377A1 (en) * | 2005-03-07 | 2008-07-03 | Dxo Labs | Method of controlling an Action, Such as a Sharpness Modification, Using a Colour Digital Image |
US20110019065A1 (en) * | 2005-03-07 | 2011-01-27 | Dxo Labs | Method for activating a function, namely an alteration of sharpness, using a colour digital image |
US7920172B2 (en) | 2005-03-07 | 2011-04-05 | Dxo Labs | Method of controlling an action, such as a sharpness modification, using a colour digital image |
WO2006095110A3 (en) * | 2005-03-07 | 2006-11-02 | Dxo Labs | Method of controlling an action, such as a sharpness modification, using a colour digital image |
WO2006095110A2 (en) * | 2005-03-07 | 2006-09-14 | Dxo Labs | Method of controlling an action, such as a sharpness modification, using a colour digital image |
US20110109749A1 (en) * | 2005-03-07 | 2011-05-12 | Dxo Labs | Method for activating a function, namely an alteration of sharpness, using a colour digital image |
KR101265358B1 (en) | 2005-03-07 | 2013-05-21 | 디엑스오 랩스 | Method of controlling an action, such as a sharpness modification, using a colour digital image |
US8212889B2 (en) | 2005-03-07 | 2012-07-03 | Dxo Labs | Method for activating a function, namely an alteration of sharpness, using a colour digital image |
EP1949671A4 (en) * | 2005-11-15 | 2008-11-05 | Nokia Corp | Imaging system with adjustable optics |
EP1949671A1 (en) * | 2005-11-15 | 2008-07-30 | Nokia Corporation | Imaging system with adjustable optics |
US8427766B2 (en) | 2006-05-08 | 2013-04-23 | Boston Scientific Scimed, Inc. | Optical assembly for medical imaging devices |
US20080225410A1 (en) * | 2006-05-08 | 2008-09-18 | Boston Scientific Scimed, Inc. | Optical assembly for medical imaging devices |
US7933079B2 (en) | 2006-05-08 | 2011-04-26 | Boston Scientific Scimed, Inc. | Optical assembly for medical imaging devices |
US20110069398A1 (en) * | 2006-05-08 | 2011-03-24 | Boston Scientific Scimed Inc. | Optical assembly for medical imaging devices |
US20080297649A1 (en) * | 2007-05-31 | 2008-12-04 | Igor Subbotin | Methods and apparatus providing light assisted automatic focus |
US7796875B2 (en) * | 2007-12-04 | 2010-09-14 | Hon Hai Precision Industry Co., Ltd. | Focusing system and method for enhancing resolution of an optical system |
US20090142046A1 (en) * | 2007-12-04 | 2009-06-04 | Hon Hai Precision Industry Co., Ltd. | Focusing system and method for enhancing resolution of an optical system |
US7929122B2 (en) | 2008-03-21 | 2011-04-19 | Abbott Point Of Care, Inc. | Method and apparatus for determining red blood cell indices of a blood sample utilizing the intrinsic pigmentation of hemoglobin contained within the red blood cells |
US20090238438A1 (en) * | 2008-03-21 | 2009-09-24 | Abbott Point Of Care, Inc. | Method and apparatus for determining red blood cell indices of a blood sample utilizing the intrinsic pigmentation of hemoglobin contained within the red blood cells |
US8361799B2 (en) | 2008-03-21 | 2013-01-29 | Abbott Point Of Care, Inc. | Method and apparatus for determining the hematocrit of a blood sample utilizing the intrinsic pigmentation of hemoglobin contained within the red blood cells |
US7903241B2 (en) | 2008-03-21 | 2011-03-08 | Abbott Point Of Care, Inc. | Method and apparatus for determining red blood cell indices of a blood sample utilizing the intrinsic pigmentation of hemoglobin contained within the red blood cells |
US20110059481A1 (en) * | 2008-03-21 | 2011-03-10 | Abbott Point Of Care, Inc. | Method and apparatus for determining red blood cell indices of a blood sample utilizing the intrinsic pigmentation of hemoglobin contained within the red blood cells |
WO2009117678A1 (en) * | 2008-03-21 | 2009-09-24 | Abbott Point Of Care, Inc. | Method and apparatus for determining a focal position of an imaging device adapted to image a biologic sample |
US8310658B2 (en) | 2008-03-21 | 2012-11-13 | Abbott Point Of Care, Inc. | Method and apparatus for identifying reticulocytes within a blood sample |
US7929121B2 (en) | 2008-03-21 | 2011-04-19 | Abbott Point Of Care, Inc. | Method and apparatus for detecting and counting platelets individually and in aggregate clumps |
US8885154B2 (en) | 2008-03-21 | 2014-11-11 | Abbott Point Of Care, Inc. | Method and apparatus for identifying reticulocytes within a blood sample |
US20090238437A1 (en) * | 2008-03-21 | 2009-09-24 | Abbott Point Of Care, Inc. | Method and apparatus for determining the hematocrit of a blood sample utilizing the intrinsic pigmentation of hemoglobin contained within the red blood cells |
US20090237665A1 (en) * | 2008-03-21 | 2009-09-24 | Abbott Point Of Care, Inc. | Method and apparatus for determining a focal position of an imaging device adapted to image a biologic sample |
US20090238439A1 (en) * | 2008-03-21 | 2009-09-24 | Abbott Point Of Care, Inc. | Method and apparatus for detecting and counting platelets individually and in aggregate clumps |
US7951599B2 (en) | 2008-03-21 | 2011-05-31 | Abbott Point Of Care, Inc. | Method and apparatus for determining the hematocrit of a blood sample utilizing the intrinsic pigmentation of hemoglobin contained within the red blood cells |
US20110149061A1 (en) * | 2008-03-21 | 2011-06-23 | Abbott Point Of Care, Inc. | Method and apparatus for identifying reticulocytes within a blood sample |
US8778687B2 (en) | 2008-03-21 | 2014-07-15 | Abbott Point Of Care, Inc. | Method and apparatus for determining the hematocrit of a blood sample utilizing the intrinsic pigmentation of hemoglobin contained within the red blood cells |
US9733233B2 (en) | 2008-03-21 | 2017-08-15 | Abbott Point Of Care, Inc. | Method and apparatus for analyzing individual cells or particulates using fluorescent quenching and/or bleaching |
US8310659B2 (en) | 2008-03-21 | 2012-11-13 | Abbott Point Of Care, Inc. | Method and apparatus for detecting and counting platelets individually and in aggregate clumps |
US8284384B2 (en) | 2008-03-21 | 2012-10-09 | Abbott Point Of Care, Inc. | Method and apparatus for analyzing individual cells or particulates using fluorescent quenching and/or bleaching |
US20110193957A1 (en) * | 2008-03-21 | 2011-08-11 | Abbott Point Of Care, Inc. | Method and apparatus for detecting and counting platelets individually and in aggregate clumps |
US8269954B2 (en) | 2008-03-21 | 2012-09-18 | Abbott Point Of Care, Inc. | Method and apparatus for analyzing individual cells or particulates using fluorescent quenching and/or bleaching |
US8502963B2 (en) | 2008-03-21 | 2013-08-06 | Abbott Point Of Care, Inc. | Method and apparatus for analyzing individual cells or particulates using fluorescent quenching and/or bleaching |
US8045165B2 (en) | 2008-03-21 | 2011-10-25 | Abbott Point Of Care, Inc. | Method and apparatus for determining a focal position of an imaging device adapted to image a biologic sample |
US8467063B2 (en) | 2008-03-21 | 2013-06-18 | Abbott Point Of Care, Inc. | Method and apparatus for determining a focal position of an imaging device adapted to image a biologic sample |
US8077296B2 (en) | 2008-03-21 | 2011-12-13 | Abbott Point Of Care, Inc. | Method and apparatus for detecting and counting platelets individually and in aggregate clumps |
US8081303B2 (en) | 2008-03-21 | 2011-12-20 | Abbott Point Of Care, Inc. | Method and apparatus for analyzing individual cells or particulates using fluorescent quenching and/or bleaching |
US20090239257A1 (en) * | 2008-03-21 | 2009-09-24 | Abbott Point Of Care, Inc. | Method and apparatus for analyzing individual cells or particulates using fluorescent quenching and/or bleaching |
US8133738B2 (en) | 2008-03-21 | 2012-03-13 | Abbott Point Of Care, Inc. | Method and apparatus for determining the hematocrit of a blood sample utilizing the intrinsic pigmentation of hemoglobin contained within the red blood cells |
US20090253218A1 (en) * | 2008-04-02 | 2009-10-08 | Abbott Point Of Care, Inc. | Method for serologic agglutination and other immunoassays performed in a thin film fluid sample |
US8221985B2 (en) | 2008-04-02 | 2012-07-17 | Abbott Point Of Care, Inc. | Self-calibrating gradient dilution in a constituent assay and gradient dilution apparatus performed in a thin film sample |
US8569076B2 (en) | 2008-04-02 | 2013-10-29 | Abbott Point Of Care, Inc. | Method for serologic agglutination and other immunoassays performed in a thin film fluid sample |
US7995194B2 (en) | 2008-04-02 | 2011-08-09 | Abbott Point Of Care, Inc. | Virtual separation of bound and free label in a ligand assay for performing immunoassays of biological fluids, including whole blood |
US8842264B2 (en) | 2008-04-02 | 2014-09-23 | Abbott Point Of Care, Inc. | Virtual separation of bound and free label in a ligand assay for performing immunoassays of biological fluids, including whole blood |
US20090252399A1 (en) * | 2008-04-02 | 2009-10-08 | Abbott Point Of Care, Inc. | Self-calibrating gradient dilution in a constituent assay and gradient dilution apparatus performed in a thin film sample |
US20090251683A1 (en) * | 2008-04-02 | 2009-10-08 | Abbott Point Of Care, Inc. | Virtual separation of bound and free label in a ligand assay for performing immunoassays of biological fluids, including whole blood |
US8319954B2 (en) | 2008-04-02 | 2012-11-27 | Abbott Point Of Care, Inc. | Virtual separation of bound and free label in a ligand assay for performing immunoassays of biological fluids, including whole blood |
US9274094B2 (en) | 2008-04-02 | 2016-03-01 | Abbott Point Of Care, Inc. | Self-calibrating gradient dilution in a constitutent assay and gradient dilution apparatus performed in a thin film sample |
US8326008B2 (en) | 2008-04-09 | 2012-12-04 | Abbott Point Of Care, Inc. | Method for measuring the area of a sample disposed within an analysis chamber |
US20090257632A1 (en) * | 2008-04-09 | 2009-10-15 | Abbott Point Of Care, Inc. | Method for measuring the area of a sample disposed within an analysis chamber |
US20090258371A1 (en) * | 2008-04-09 | 2009-10-15 | Abbott Point Of Care, Inc. | Method of detecting very low levels of analyte within a thin film fluid sample contained in a thin thickness chamber |
US9094661B2 (en) | 2008-05-20 | 2015-07-28 | Pelican Imaging Corporation | Systems and methods for generating depth maps using a set of images containing a baseline image |
US9077893B2 (en) | 2008-05-20 | 2015-07-07 | Pelican Imaging Corporation | Capturing and processing of images captured by non-grid camera arrays |
US9049391B2 (en) | 2008-05-20 | 2015-06-02 | Pelican Imaging Corporation | Capturing and processing of near-IR images including occlusions using camera arrays incorporating near-IR light sources |
US9049367B2 (en) | 2008-05-20 | 2015-06-02 | Pelican Imaging Corporation | Systems and methods for synthesizing higher resolution images using images captured by camera arrays |
US9041823B2 (en) | 2008-05-20 | 2015-05-26 | Pelican Imaging Corporation | Systems and methods for performing post capture refocus using images captured by camera arrays |
US9041829B2 (en) | 2008-05-20 | 2015-05-26 | Pelican Imaging Corporation | Capturing and processing of high dynamic range images using camera arrays |
US8885059B1 (en) | 2008-05-20 | 2014-11-11 | Pelican Imaging Corporation | Systems and methods for measuring depth using images captured by camera arrays |
US9049411B2 (en) | 2008-05-20 | 2015-06-02 | Pelican Imaging Corporation | Camera arrays incorporating 3×3 imager configurations |
US9055233B2 (en) | 2008-05-20 | 2015-06-09 | Pelican Imaging Corporation | Systems and methods for synthesizing higher resolution images using a set of images containing a baseline image |
US9055213B2 (en) | 2008-05-20 | 2015-06-09 | Pelican Imaging Corporation | Systems and methods for measuring depth using images captured by monolithic camera arrays including at least one bayer camera |
US9060121B2 (en) | 2008-05-20 | 2015-06-16 | Pelican Imaging Corporation | Capturing and processing of images captured by camera arrays including cameras dedicated to sampling luma and cameras dedicated to sampling chroma |
US9060142B2 (en) | 2008-05-20 | 2015-06-16 | Pelican Imaging Corporation | Capturing and processing of images captured by camera arrays including heterogeneous optics |
US9749547B2 (en) | 2008-05-20 | 2017-08-29 | Fotonation Cayman Limited | Capturing and processing of images using camera array incorperating Bayer cameras having different fields of view |
US9060120B2 (en) | 2008-05-20 | 2015-06-16 | Pelican Imaging Corporation | Systems and methods for generating depth maps using images captured by camera arrays |
US9712759B2 (en) | 2008-05-20 | 2017-07-18 | Fotonation Cayman Limited | Systems and methods for generating depth maps using a camera arrays incorporating monochrome and color cameras |
US10027901B2 (en) | 2008-05-20 | 2018-07-17 | Fotonation Cayman Limited | Systems and methods for generating depth maps using a camera arrays incorporating monochrome and color cameras |
US9124815B2 (en) | 2008-05-20 | 2015-09-01 | Pelican Imaging Corporation | Capturing and processing of images including occlusions captured by arrays of luma and chroma cameras |
US9576369B2 (en) | 2008-05-20 | 2017-02-21 | Fotonation Cayman Limited | Systems and methods for generating depth maps using images captured by camera arrays incorporating cameras having different fields of view |
US11792538B2 (en) | 2008-05-20 | 2023-10-17 | Adeia Imaging Llc | Capturing and processing of images including occlusions focused on an image sensor by a lens stack array |
US9188765B2 (en) | 2008-05-20 | 2015-11-17 | Pelican Imaging Corporation | Capturing and processing of images including occlusions focused on an image sensor by a lens stack array |
US9191580B2 (en) | 2008-05-20 | 2015-11-17 | Pelican Imaging Corporation | Capturing and processing of images including occlusions captured by camera arrays |
US9235898B2 (en) | 2008-05-20 | 2016-01-12 | Pelican Imaging Corporation | Systems and methods for generating depth maps using light focused on an image sensor by a lens element array |
US10142560B2 (en) | 2008-05-20 | 2018-11-27 | Fotonation Limited | Capturing and processing of images including occlusions focused on an image sensor by a lens stack array |
US9485496B2 (en) | 2008-05-20 | 2016-11-01 | Pelican Imaging Corporation | Systems and methods for measuring depth using images captured by a camera array including cameras surrounding a central camera |
US8866920B2 (en) | 2008-05-20 | 2014-10-21 | Pelican Imaging Corporation | Capturing and processing of images using monolithic camera array with heterogeneous imagers |
US11412158B2 (en) | 2008-05-20 | 2022-08-09 | Fotonation Limited | Capturing and processing of images including occlusions focused on an image sensor by a lens stack array |
US9049390B2 (en) | 2008-05-20 | 2015-06-02 | Pelican Imaging Corporation | Capturing and processing of images captured by arrays including polychromatic cameras |
US9049381B2 (en) | 2008-05-20 | 2015-06-02 | Pelican Imaging Corporation | Systems and methods for normalizing image data captured by camera arrays |
US9060124B2 (en) | 2008-05-20 | 2015-06-16 | Pelican Imaging Corporation | Capturing and processing of images using non-monolithic camera arrays |
US8896719B1 (en) | 2008-05-20 | 2014-11-25 | Pelican Imaging Corporation | Systems and methods for parallax measurement using camera arrays incorporating 3 x 3 camera configurations |
US8902321B2 (en) | 2008-05-20 | 2014-12-02 | Pelican Imaging Corporation | Capturing and processing of images using monolithic camera array with heterogeneous imagers |
US10140724B2 (en) | 2009-01-12 | 2018-11-27 | Intermec Ip Corporation | Semi-automatic dimensioning with imager on a portable device |
US10845184B2 (en) | 2009-01-12 | 2020-11-24 | Intermec Ip Corporation | Semi-automatic dimensioning with imager on a portable device |
US20100255605A1 (en) * | 2009-04-02 | 2010-10-07 | Abbott Point Of Care, Inc. | Method and device for transferring biologic fluid samples |
US8390732B2 (en) * | 2009-10-27 | 2013-03-05 | Hon Hai Precision Industry Co., Ltd. | Method for adjusting a focal length of a camera |
US20110096175A1 (en) * | 2009-10-27 | 2011-04-28 | Hon Hai Precision Industry Co., Ltd. | Method for adjusting a focal length of a camera |
US10306120B2 (en) | 2009-11-20 | 2019-05-28 | Fotonation Limited | Capturing and processing of images captured by camera arrays incorporating cameras with telephoto and conventional lenses to generate depth maps |
US9264610B2 (en) | 2009-11-20 | 2016-02-16 | Pelican Imaging Corporation | Capturing and processing of images including occlusions captured by heterogeneous camera arrays |
US8861089B2 (en) | 2009-11-20 | 2014-10-14 | Pelican Imaging Corporation | Capturing and processing of images using monolithic camera array with heterogeneous imagers |
US8837803B2 (en) | 2009-12-31 | 2014-09-16 | Abbott Point Of Care, Inc. | Method and apparatus for determining mean cell volume of red blood cells |
US20110164803A1 (en) * | 2009-12-31 | 2011-07-07 | Abbott Point Of Care, Inc. | Method and apparatus for determining mean cell volume of red blood cells |
EP2362258A1 (en) * | 2010-01-12 | 2011-08-31 | Nikon Corporation | Image-capturing device |
US8958009B2 (en) * | 2010-01-12 | 2015-02-17 | Nikon Corporation | Image-capturing device |
CN102129146A (en) * | 2010-01-12 | 2011-07-20 | 株式会社尼康 | Image pickup device |
US20110199534A1 (en) * | 2010-01-12 | 2011-08-18 | Nikon Corporation | Image-capturing device |
US8781203B2 (en) | 2010-03-18 | 2014-07-15 | Abbott Point Of Care, Inc. | Method and apparatus for determining at least one hemoglobin related parameter of a whole blood sample |
US8472693B2 (en) | 2010-03-18 | 2013-06-25 | Abbott Point Of Care, Inc. | Method for determining at least one hemoglobin related parameter of a whole blood sample |
US20110261208A1 (en) * | 2010-04-27 | 2011-10-27 | Ability Enterprise Co., Ltd. | Method of automatically calibrating a visual parameter for an imaging device |
US8310549B2 (en) * | 2010-04-27 | 2012-11-13 | Ability Enterprise Co., Ltd. | Method of automatically calibrating a visual parameter for an imaging device |
US9936148B2 (en) | 2010-05-12 | 2018-04-03 | Fotonation Cayman Limited | Imager array interfaces |
US10455168B2 (en) | 2010-05-12 | 2019-10-22 | Fotonation Limited | Imager array interfaces |
US8928793B2 (en) | 2010-05-12 | 2015-01-06 | Pelican Imaging Corporation | Imager array interfaces |
US9398205B2 (en) * | 2010-09-01 | 2016-07-19 | Apple Inc. | Auto-focus control using image statistics data with coarse and fine auto-focus scores |
US20120051730A1 (en) * | 2010-09-01 | 2012-03-01 | Apple Inc. | Auto-focus control using image statistics data with coarse and fine auto-focus scores |
US11875475B2 (en) | 2010-12-14 | 2024-01-16 | Adeia Imaging Llc | Systems and methods for synthesizing high resolution images using images captured by an array of independently controllable imagers |
US9361662B2 (en) | 2010-12-14 | 2016-06-07 | Pelican Imaging Corporation | Systems and methods for synthesizing high resolution images using images captured by an array of independently controllable imagers |
US9047684B2 (en) | 2010-12-14 | 2015-06-02 | Pelican Imaging Corporation | Systems and methods for synthesizing high resolution images using a set of geometrically registered images |
US8878950B2 (en) | 2010-12-14 | 2014-11-04 | Pelican Imaging Corporation | Systems and methods for synthesizing high resolution images using super-resolution processes |
US11423513B2 (en) | 2010-12-14 | 2022-08-23 | Fotonation Limited | Systems and methods for synthesizing high resolution images using images captured by an array of independently controllable imagers |
US9041824B2 (en) | 2010-12-14 | 2015-05-26 | Pelican Imaging Corporation | Systems and methods for dynamic refocusing of high resolution images generated using images captured by a plurality of imagers |
US10366472B2 (en) | 2010-12-14 | 2019-07-30 | Fotonation Limited | Systems and methods for synthesizing high resolution images using images captured by an array of independently controllable imagers |
US20130057753A1 (en) * | 2011-03-28 | 2013-03-07 | Intermec Ip Corp. | Two-dimensional imager with solid-state auto-focus |
US8988590B2 (en) * | 2011-03-28 | 2015-03-24 | Intermec Ip Corp. | Two-dimensional imager with solid-state auto-focus |
US9253393B2 (en) | 2011-03-28 | 2016-02-02 | Intermec Ip, Corp. | Two-dimensional imager with solid-state auto-focus |
US9866739B2 (en) | 2011-05-11 | 2018-01-09 | Fotonation Cayman Limited | Systems and methods for transmitting and receiving array camera image data |
US9197821B2 (en) | 2011-05-11 | 2015-11-24 | Pelican Imaging Corporation | Systems and methods for transmitting and receiving array camera image data |
US10742861B2 (en) | 2011-05-11 | 2020-08-11 | Fotonation Limited | Systems and methods for transmitting and receiving array camera image data |
US10218889B2 (en) | 2011-05-11 | 2019-02-26 | Fotonation Limited | Systems and methods for transmitting and receiving array camera image data |
US20120307089A1 (en) * | 2011-05-31 | 2012-12-06 | Apple Inc. | Estimating optical characteristics of a camera component using sharpness sweep data |
US8711275B2 (en) * | 2011-05-31 | 2014-04-29 | Apple Inc. | Estimating optical characteristics of a camera component using sharpness sweep data |
US9128228B2 (en) | 2011-06-28 | 2015-09-08 | Pelican Imaging Corporation | Optical arrangements for use with an array camera |
US9516222B2 (en) | 2011-06-28 | 2016-12-06 | Kip Peli P1 Lp | Array cameras incorporating monolithic array camera modules with high MTF lens stacks for capture of images used in super-resolution processing |
US9578237B2 (en) | 2011-06-28 | 2017-02-21 | Fotonation Cayman Limited | Array cameras incorporating optics with modulation transfer functions greater than sensor Nyquist frequency for capture of images used in super-resolution processing |
US20130050546A1 (en) * | 2011-08-30 | 2013-02-28 | Canon Kabushiki Kaisha | Image processing apparatus and method |
US8754957B2 (en) * | 2011-08-30 | 2014-06-17 | Canon Kabushiki Kaisha | Image processing apparatus and method |
US9392159B2 (en) | 2011-09-02 | 2016-07-12 | Nikon Corporation | Focus estimating device, imaging device, and storage medium storing image processing program |
US9648227B2 (en) | 2011-09-02 | 2017-05-09 | Nikon Corporation | Focus estimating device, imaging device, and storage medium storing image processing program |
US9794476B2 (en) | 2011-09-19 | 2017-10-17 | Fotonation Cayman Limited | Systems and methods for controlling aliasing in images captured by an array camera for use in super resolution processing using pixel apertures |
US10375302B2 (en) | 2011-09-19 | 2019-08-06 | Fotonation Limited | Systems and methods for controlling aliasing in images captured by an array camera for use in super resolution processing using pixel apertures |
US11729365B2 (en) | 2011-09-28 | 2023-08-15 | Adela Imaging LLC | Systems and methods for encoding image files containing depth maps stored as metadata |
US9031343B2 (en) | 2011-09-28 | 2015-05-12 | Pelican Imaging Corporation | Systems and methods for encoding light field image files having a depth map |
US10430682B2 (en) | 2011-09-28 | 2019-10-01 | Fotonation Limited | Systems and methods for decoding image files containing depth maps stored as metadata |
US9129183B2 (en) | 2011-09-28 | 2015-09-08 | Pelican Imaging Corporation | Systems and methods for encoding light field image files |
US9025894B2 (en) | 2011-09-28 | 2015-05-05 | Pelican Imaging Corporation | Systems and methods for decoding light field image files having depth and confidence maps |
US9025895B2 (en) | 2011-09-28 | 2015-05-05 | Pelican Imaging Corporation | Systems and methods for decoding refocusable light field image files |
US9031335B2 (en) | 2011-09-28 | 2015-05-12 | Pelican Imaging Corporation | Systems and methods for encoding light field image files having depth and confidence maps |
US9042667B2 (en) | 2011-09-28 | 2015-05-26 | Pelican Imaging Corporation | Systems and methods for decoding light field image files using a depth map |
US10984276B2 (en) | 2011-09-28 | 2021-04-20 | Fotonation Limited | Systems and methods for encoding image files containing depth maps stored as metadata |
US9036928B2 (en) | 2011-09-28 | 2015-05-19 | Pelican Imaging Corporation | Systems and methods for encoding structured light field image files |
US9036931B2 (en) | 2011-09-28 | 2015-05-19 | Pelican Imaging Corporation | Systems and methods for decoding structured light field image files |
US8831367B2 (en) | 2011-09-28 | 2014-09-09 | Pelican Imaging Corporation | Systems and methods for decoding light field image files |
US10019816B2 (en) | 2011-09-28 | 2018-07-10 | Fotonation Cayman Limited | Systems and methods for decoding image files containing depth maps stored as metadata |
US20180197035A1 (en) | 2011-09-28 | 2018-07-12 | Fotonation Cayman Limited | Systems and Methods for Encoding Image Files Containing Depth Maps Stored as Metadata |
US9811753B2 (en) | 2011-09-28 | 2017-11-07 | Fotonation Cayman Limited | Systems and methods for encoding light field image files |
US9031342B2 (en) | 2011-09-28 | 2015-05-12 | Pelican Imaging Corporation | Systems and methods for encoding refocusable light field image files |
US10275676B2 (en) | 2011-09-28 | 2019-04-30 | Fotonation Limited | Systems and methods for encoding image files containing depth maps stored as metadata |
US9536166B2 (en) | 2011-09-28 | 2017-01-03 | Kip Peli P1 Lp | Systems and methods for decoding image files containing depth maps stored as metadata |
US9864921B2 (en) | 2011-09-28 | 2018-01-09 | Fotonation Cayman Limited | Systems and methods for encoding image files containing depth maps stored as metadata |
US8803973B2 (en) * | 2011-11-09 | 2014-08-12 | Hon Hai Precision Industry Co., Ltd. | Stereo image capturing device |
US20130113889A1 (en) * | 2011-11-09 | 2013-05-09 | Hon Hai Precision Industry Co., Ltd. | Stereo image capturing device |
US20150009394A1 (en) * | 2012-01-17 | 2015-01-08 | Nokia Corporation | Focusing control method using colour channel analysis |
US9386214B2 (en) * | 2012-01-17 | 2016-07-05 | Nokia Technologies Oy | Focusing control method using colour channel analysis |
US9754422B2 (en) | 2012-02-21 | 2017-09-05 | Fotonation Cayman Limited | Systems and method for performing depth based image editing |
US9412206B2 (en) | 2012-02-21 | 2016-08-09 | Pelican Imaging Corporation | Systems and methods for the manipulation of captured light field image data |
US10311649B2 (en) | 2012-02-21 | 2019-06-04 | Fotonation Limited | Systems and method for performing depth based image editing |
US8619182B2 (en) | 2012-03-06 | 2013-12-31 | Csr Technology Inc. | Fast auto focus techniques for digital cameras |
US9639947B2 (en) | 2012-04-18 | 2017-05-02 | Sony Corporation | Method and optical system for determining a depth map of an image |
US9210392B2 (en) | 2012-05-01 | 2015-12-08 | Pelican Imaging Coporation | Camera modules patterned with pi filter groups |
US9706132B2 (en) | 2012-05-01 | 2017-07-11 | Fotonation Cayman Limited | Camera modules patterned with pi filter groups |
US9779546B2 (en) | 2012-05-04 | 2017-10-03 | Intermec Ip Corp. | Volume dimensioning systems and methods |
US10467806B2 (en) | 2012-05-04 | 2019-11-05 | Intermec Ip Corp. | Volume dimensioning systems and methods |
US9007368B2 (en) | 2012-05-07 | 2015-04-14 | Intermec Ip Corp. | Dimensioning system calibration systems and methods |
US9292969B2 (en) | 2012-05-07 | 2016-03-22 | Intermec Ip Corp. | Dimensioning system calibration systems and methods |
US10007858B2 (en) | 2012-05-15 | 2018-06-26 | Honeywell International Inc. | Terminals and methods for dimensioning objects |
US10635922B2 (en) | 2012-05-15 | 2020-04-28 | Hand Held Products, Inc. | Terminals and methods for dimensioning objects |
US8917336B2 (en) | 2012-05-31 | 2014-12-23 | Apple Inc. | Image signal processing involving geometric distortion correction |
US11689826B2 (en) | 2012-05-31 | 2023-06-27 | Apple Inc. | Systems and method for reducing fixed pattern noise in image data |
US9031319B2 (en) | 2012-05-31 | 2015-05-12 | Apple Inc. | Systems and methods for luma sharpening |
US9317930B2 (en) | 2012-05-31 | 2016-04-19 | Apple Inc. | Systems and methods for statistics collection using pixel mask |
US11089247B2 (en) | 2012-05-31 | 2021-08-10 | Apple Inc. | Systems and method for reducing fixed pattern noise in image data |
US9142012B2 (en) | 2012-05-31 | 2015-09-22 | Apple Inc. | Systems and methods for chroma noise reduction |
US9131196B2 (en) | 2012-05-31 | 2015-09-08 | Apple Inc. | Systems and methods for defective pixel correction with neighboring pixels |
US9025867B2 (en) | 2012-05-31 | 2015-05-05 | Apple Inc. | Systems and methods for YCC image processing |
US8872946B2 (en) | 2012-05-31 | 2014-10-28 | Apple Inc. | Systems and methods for raw image processing |
US9077943B2 (en) | 2012-05-31 | 2015-07-07 | Apple Inc. | Local image statistics collection |
US9743057B2 (en) | 2012-05-31 | 2017-08-22 | Apple Inc. | Systems and methods for lens shading correction |
US9332239B2 (en) | 2012-05-31 | 2016-05-03 | Apple Inc. | Systems and methods for RGB image processing |
US8817120B2 (en) | 2012-05-31 | 2014-08-26 | Apple Inc. | Systems and methods for collecting fixed pattern noise statistics of image data |
US9105078B2 (en) | 2012-05-31 | 2015-08-11 | Apple Inc. | Systems and methods for local tone mapping |
US9741099B2 (en) | 2012-05-31 | 2017-08-22 | Apple Inc. | Systems and methods for local tone mapping |
US8953882B2 (en) | 2012-05-31 | 2015-02-10 | Apple Inc. | Systems and methods for determining noise statistics of image data |
US9342858B2 (en) | 2012-05-31 | 2016-05-17 | Apple Inc. | Systems and methods for statistics collection using clipped pixel tracking |
US9014504B2 (en) | 2012-05-31 | 2015-04-21 | Apple Inc. | Systems and methods for highlight recovery in an image signal processor |
US9710896B2 (en) | 2012-05-31 | 2017-07-18 | Apple Inc. | Systems and methods for chroma noise reduction |
US9100635B2 (en) | 2012-06-28 | 2015-08-04 | Pelican Imaging Corporation | Systems and methods for detecting defective camera arrays and optic arrays |
US10334241B2 (en) | 2012-06-28 | 2019-06-25 | Fotonation Limited | Systems and methods for detecting defective camera arrays and optic arrays |
US9807382B2 (en) | 2012-06-28 | 2017-10-31 | Fotonation Cayman Limited | Systems and methods for detecting defective camera arrays and optic arrays |
US20140002674A1 (en) * | 2012-06-30 | 2014-01-02 | Pelican Imaging Corporation | Systems and Methods for Manufacturing Camera Modules Using Active Alignment of Lens Stack Arrays and Sensors |
US10261219B2 (en) | 2012-06-30 | 2019-04-16 | Fotonation Limited | Systems and methods for manufacturing camera modules using active alignment of lens stack arrays and sensors |
US9766380B2 (en) | 2012-06-30 | 2017-09-19 | Fotonation Cayman Limited | Systems and methods for manufacturing camera modules using active alignment of lens stack arrays and sensors |
CN104520754A (en) * | 2012-06-30 | 2015-04-15 | 派力肯影像公司 | Systems and methods for manufacturing camera modules using active alignment of lens stack arrays and sensors |
US11022725B2 (en) | 2012-06-30 | 2021-06-01 | Fotonation Limited | Systems and methods for manufacturing camera modules using active alignment of lens stack arrays and sensors |
WO2014004134A1 (en) * | 2012-06-30 | 2014-01-03 | Pelican Imaging Corporation | Systems and methods for manufacturing camera modules using active alignment of lens stack arrays and sensors |
US10321127B2 (en) | 2012-08-20 | 2019-06-11 | Intermec Ip Corp. | Volume dimensioning system calibration systems and methods |
US10805603B2 (en) | 2012-08-20 | 2020-10-13 | Intermec Ip Corp. | Volume dimensioning system calibration systems and methods |
US9129377B2 (en) | 2012-08-21 | 2015-09-08 | Pelican Imaging Corporation | Systems and methods for measuring depth based upon occlusion patterns in images |
US9147254B2 (en) | 2012-08-21 | 2015-09-29 | Pelican Imaging Corporation | Systems and methods for measuring depth in the presence of occlusions using a subset of images |
US9123117B2 (en) | 2012-08-21 | 2015-09-01 | Pelican Imaging Corporation | Systems and methods for generating depth maps and corresponding confidence maps indicating depth estimation reliability |
US9123118B2 (en) | 2012-08-21 | 2015-09-01 | Pelican Imaging Corporation | System and methods for measuring depth using an array camera employing a bayer filter |
US9235900B2 (en) | 2012-08-21 | 2016-01-12 | Pelican Imaging Corporation | Systems and methods for estimating depth and visibility from a reference viewpoint for pixels in a set of images captured from different viewpoints |
US9240049B2 (en) | 2012-08-21 | 2016-01-19 | Pelican Imaging Corporation | Systems and methods for measuring depth using an array of independently controllable cameras |
US10380752B2 (en) | 2012-08-21 | 2019-08-13 | Fotonation Limited | Systems and methods for estimating depth and visibility from a reference viewpoint for pixels in a set of images captured from different viewpoints |
US9858673B2 (en) | 2012-08-21 | 2018-01-02 | Fotonation Cayman Limited | Systems and methods for estimating depth and visibility from a reference viewpoint for pixels in a set of images captured from different viewpoints |
US10462362B2 (en) | 2012-08-23 | 2019-10-29 | Fotonation Limited | Feature based high resolution motion estimation from low resolution images captured using an array source |
US9813616B2 (en) | 2012-08-23 | 2017-11-07 | Fotonation Cayman Limited | Feature based high resolution motion estimation from low resolution images captured using an array source |
US9214013B2 (en) | 2012-09-14 | 2015-12-15 | Pelican Imaging Corporation | Systems and methods for correcting user identified artifacts in light field images |
US10390005B2 (en) | 2012-09-28 | 2019-08-20 | Fotonation Limited | Generating images from light fields utilizing virtual viewpoints |
US9939259B2 (en) | 2012-10-04 | 2018-04-10 | Hand Held Products, Inc. | Measuring object dimensions using mobile computer |
US9841311B2 (en) | 2012-10-16 | 2017-12-12 | Hand Held Products, Inc. | Dimensioning system |
US10908013B2 (en) | 2012-10-16 | 2021-02-02 | Hand Held Products, Inc. | Dimensioning system |
US9143711B2 (en) | 2012-11-13 | 2015-09-22 | Pelican Imaging Corporation | Systems and methods for array camera focal plane control |
US9749568B2 (en) | 2012-11-13 | 2017-08-29 | Fotonation Cayman Limited | Systems and methods for array camera focal plane control |
US9462164B2 (en) | 2013-02-21 | 2016-10-04 | Pelican Imaging Corporation | Systems and methods for generating compressed light field representation data using captured light fields, array geometry, and parallax information |
US10009538B2 (en) | 2013-02-21 | 2018-06-26 | Fotonation Cayman Limited | Systems and methods for generating compressed light field representation data using captured light fields, array geometry, and parallax information |
US9774831B2 (en) | 2013-02-24 | 2017-09-26 | Fotonation Cayman Limited | Thin form factor computational array cameras and modular array cameras |
US9743051B2 (en) | 2013-02-24 | 2017-08-22 | Fotonation Cayman Limited | Thin form factor computational array cameras and modular array cameras |
US9374512B2 (en) | 2013-02-24 | 2016-06-21 | Pelican Imaging Corporation | Thin form factor computational array cameras and modular array cameras |
US9253380B2 (en) | 2013-02-24 | 2016-02-02 | Pelican Imaging Corporation | Thin form factor computational array cameras and modular array cameras |
US9638883B1 (en) | 2013-03-04 | 2017-05-02 | Fotonation Cayman Limited | Passive alignment of array camera modules constructed from lens stack arrays and sensors based upon alignment information obtained during manufacture of array camera modules using an active alignment process |
US9917998B2 (en) | 2013-03-08 | 2018-03-13 | Fotonation Cayman Limited | Systems and methods for measuring scene information while capturing images using array cameras |
US9774789B2 (en) | 2013-03-08 | 2017-09-26 | Fotonation Cayman Limited | Systems and methods for high dynamic range imaging using array cameras |
US8866912B2 (en) | 2013-03-10 | 2014-10-21 | Pelican Imaging Corporation | System and methods for calibration of an array camera using a single captured image |
US9124864B2 (en) | 2013-03-10 | 2015-09-01 | Pelican Imaging Corporation | System and methods for calibration of an array camera |
US10958892B2 (en) | 2013-03-10 | 2021-03-23 | Fotonation Limited | System and methods for calibration of an array camera |
US10225543B2 (en) | 2013-03-10 | 2019-03-05 | Fotonation Limited | System and methods for calibration of an array camera |
US11272161B2 (en) | 2013-03-10 | 2022-03-08 | Fotonation Limited | System and methods for calibration of an array camera |
US9986224B2 (en) | 2013-03-10 | 2018-05-29 | Fotonation Cayman Limited | System and methods for calibration of an array camera |
US11570423B2 (en) | 2013-03-10 | 2023-01-31 | Adeia Imaging Llc | System and methods for calibration of an array camera |
US9521416B1 (en) | 2013-03-11 | 2016-12-13 | Kip Peli P1 Lp | Systems and methods for image data compression |
US9124831B2 (en) | 2013-03-13 | 2015-09-01 | Pelican Imaging Corporation | System and methods for calibration of an array camera |
US9800856B2 (en) | 2013-03-13 | 2017-10-24 | Fotonation Cayman Limited | Systems and methods for synthesizing images from image data captured by an array camera using restricted depth of field depth maps in which depth estimation precision varies |
US9741118B2 (en) | 2013-03-13 | 2017-08-22 | Fotonation Cayman Limited | System and methods for calibration of an array camera |
US9519972B2 (en) | 2013-03-13 | 2016-12-13 | Kip Peli P1 Lp | Systems and methods for synthesizing images from image data captured by an array camera using restricted depth of field depth maps in which depth estimation precision varies |
US9888194B2 (en) | 2013-03-13 | 2018-02-06 | Fotonation Cayman Limited | Array camera architecture implementing quantum film image sensors |
US9080856B2 (en) | 2013-03-13 | 2015-07-14 | Intermec Ip Corp. | Systems and methods for enhancing dimensioning, for example volume dimensioning |
US10127682B2 (en) | 2013-03-13 | 2018-11-13 | Fotonation Limited | System and methods for calibration of an array camera |
US9733486B2 (en) | 2013-03-13 | 2017-08-15 | Fotonation Cayman Limited | Systems and methods for controlling aliasing in images captured by an array camera for use in super-resolution processing |
US9784566B2 (en) | 2013-03-13 | 2017-10-10 | Intermec Ip Corp. | Systems and methods for enhancing dimensioning |
US9106784B2 (en) | 2013-03-13 | 2015-08-11 | Pelican Imaging Corporation | Systems and methods for controlling aliasing in images captured by an array camera for use in super-resolution processing |
US10412314B2 (en) | 2013-03-14 | 2019-09-10 | Fotonation Limited | Systems and methods for photometric normalization in array cameras |
US10547772B2 (en) | 2013-03-14 | 2020-01-28 | Fotonation Limited | Systems and methods for reducing motion blur in images or video in ultra low light with array cameras |
US9100586B2 (en) | 2013-03-14 | 2015-08-04 | Pelican Imaging Corporation | Systems and methods for photometric normalization in array cameras |
US10091405B2 (en) | 2013-03-14 | 2018-10-02 | Fotonation Cayman Limited | Systems and methods for reducing motion blur in images or video in ultra low light with array cameras |
US9787911B2 (en) | 2013-03-14 | 2017-10-10 | Fotonation Cayman Limited | Systems and methods for photometric normalization in array cameras |
US9578259B2 (en) | 2013-03-14 | 2017-02-21 | Fotonation Cayman Limited | Systems and methods for reducing motion blur in images or video in ultra low light with array cameras |
US9497370B2 (en) | 2013-03-15 | 2016-11-15 | Pelican Imaging Corporation | Array camera architecture implementing quantum dot color filters |
US9602805B2 (en) | 2013-03-15 | 2017-03-21 | Fotonation Cayman Limited | Systems and methods for estimating depth using ad hoc stereo array cameras |
US9497429B2 (en) | 2013-03-15 | 2016-11-15 | Pelican Imaging Corporation | Extended color processing on pelican array cameras |
US10122993B2 (en) | 2013-03-15 | 2018-11-06 | Fotonation Limited | Autofocus system for a conventional camera that uses depth information from an array camera |
US10638099B2 (en) | 2013-03-15 | 2020-04-28 | Fotonation Limited | Extended color processing on pelican array cameras |
US9445003B1 (en) | 2013-03-15 | 2016-09-13 | Pelican Imaging Corporation | Systems and methods for synthesizing high resolution images using image deconvolution based on motion and depth information |
US9955070B2 (en) | 2013-03-15 | 2018-04-24 | Fotonation Cayman Limited | Systems and methods for synthesizing high resolution images using image deconvolution based on motion and depth information |
US9800859B2 (en) | 2013-03-15 | 2017-10-24 | Fotonation Cayman Limited | Systems and methods for estimating depth using stereo array cameras |
US10182216B2 (en) | 2013-03-15 | 2019-01-15 | Fotonation Limited | Extended color processing on pelican array cameras |
US10455218B2 (en) | 2013-03-15 | 2019-10-22 | Fotonation Limited | Systems and methods for estimating depth using stereo array cameras |
US10542208B2 (en) | 2013-03-15 | 2020-01-21 | Fotonation Limited | Systems and methods for synthesizing high resolution images using image deconvolution based on motion and depth information |
US10674138B2 (en) | 2013-03-15 | 2020-06-02 | Fotonation Limited | Autofocus system for a conventional camera that uses depth information from an array camera |
US9438888B2 (en) | 2013-03-15 | 2016-09-06 | Pelican Imaging Corporation | Systems and methods for stereo imaging with camera arrays |
US9633442B2 (en) | 2013-03-15 | 2017-04-25 | Fotonation Cayman Limited | Array cameras including an array camera module augmented with a separate camera |
US10228452B2 (en) | 2013-06-07 | 2019-03-12 | Hand Held Products, Inc. | Method of error correction for 3D imaging device |
US10203402B2 (en) | 2013-06-07 | 2019-02-12 | Hand Held Products, Inc. | Method of error correction for 3D imaging device |
US9239950B2 (en) | 2013-07-01 | 2016-01-19 | Hand Held Products, Inc. | Dimensioning system |
US9464885B2 (en) | 2013-08-30 | 2016-10-11 | Hand Held Products, Inc. | System and method for package dimensioning |
US10540806B2 (en) | 2013-09-27 | 2020-01-21 | Fotonation Limited | Systems and methods for depth-assisted perspective distortion correction |
US9898856B2 (en) | 2013-09-27 | 2018-02-20 | Fotonation Cayman Limited | Systems and methods for depth-assisted perspective distortion correction |
US9426343B2 (en) | 2013-11-07 | 2016-08-23 | Pelican Imaging Corporation | Array cameras incorporating independently aligned lens stacks |
US9264592B2 (en) | 2013-11-07 | 2016-02-16 | Pelican Imaging Corporation | Array camera modules incorporating independently aligned lens stacks |
US9185276B2 (en) | 2013-11-07 | 2015-11-10 | Pelican Imaging Corporation | Methods of manufacturing array camera modules incorporating independently aligned lens stacks |
US9924092B2 (en) | 2013-11-07 | 2018-03-20 | Fotonation Cayman Limited | Array cameras incorporating independently aligned lens stacks |
US11486698B2 (en) | 2013-11-18 | 2022-11-01 | Fotonation Limited | Systems and methods for estimating depth from projected texture using camera arrays |
US10767981B2 (en) | 2013-11-18 | 2020-09-08 | Fotonation Limited | Systems and methods for estimating depth from projected texture using camera arrays |
US9083885B1 (en) * | 2013-11-18 | 2015-07-14 | Google Inc. | Fixed focus camera module with near-field image recognition |
US10119808B2 (en) | 2013-11-18 | 2018-11-06 | Fotonation Limited | Systems and methods for estimating depth from projected texture using camera arrays |
US9456134B2 (en) | 2013-11-26 | 2016-09-27 | Pelican Imaging Corporation | Array camera configurations incorporating constituent array cameras and constituent cameras |
US9426361B2 (en) | 2013-11-26 | 2016-08-23 | Pelican Imaging Corporation | Array camera configurations incorporating multiple constituent array cameras |
US9813617B2 (en) | 2013-11-26 | 2017-11-07 | Fotonation Cayman Limited | Array camera configurations incorporating constituent array cameras and constituent cameras |
US10708492B2 (en) | 2013-11-26 | 2020-07-07 | Fotonation Limited | Array camera configurations incorporating constituent array cameras and constituent cameras |
US10574905B2 (en) | 2014-03-07 | 2020-02-25 | Fotonation Limited | System and methods for depth regularization and semiautomatic interactive matting using RGB-D images |
US10089740B2 (en) | 2014-03-07 | 2018-10-02 | Fotonation Limited | System and methods for depth regularization and semiautomatic interactive matting using RGB-D images |
US9247117B2 (en) | 2014-04-07 | 2016-01-26 | Pelican Imaging Corporation | Systems and methods for correcting for warpage of a sensor array in an array camera module by introducing warpage into a focal plane of a lens stack array |
US9521319B2 (en) | 2014-06-18 | 2016-12-13 | Pelican Imaging Corporation | Array cameras and array camera modules including spectral filters disposed outside of a constituent image sensor |
US10240914B2 (en) | 2014-08-06 | 2019-03-26 | Hand Held Products, Inc. | Dimensioning system with guided alignment |
US9823059B2 (en) | 2014-08-06 | 2017-11-21 | Hand Held Products, Inc. | Dimensioning system with guided alignment |
US10250871B2 (en) | 2014-09-29 | 2019-04-02 | Fotonation Limited | Systems and methods for dynamic calibration of array cameras |
US11546576B2 (en) | 2014-09-29 | 2023-01-03 | Adeia Imaging Llc | Systems and methods for dynamic calibration of array cameras |
US10859375B2 (en) | 2014-10-10 | 2020-12-08 | Hand Held Products, Inc. | Methods for improving the accuracy of dimensioning-system measurements |
US9779276B2 (en) | 2014-10-10 | 2017-10-03 | Hand Held Products, Inc. | Depth sensor based auto-focus system for an indicia scanner |
US10775165B2 (en) | 2014-10-10 | 2020-09-15 | Hand Held Products, Inc. | Methods for improving the accuracy of dimensioning-system measurements |
US10402956B2 (en) | 2014-10-10 | 2019-09-03 | Hand Held Products, Inc. | Image-stitching for dimensioning |
US10134120B2 (en) | 2014-10-10 | 2018-11-20 | Hand Held Products, Inc. | Image-stitching for dimensioning |
US10121039B2 (en) | 2014-10-10 | 2018-11-06 | Hand Held Products, Inc. | Depth sensor based auto-focus system for an indicia scanner |
US10810715B2 (en) | 2014-10-10 | 2020-10-20 | Hand Held Products, Inc | System and method for picking validation |
US9752864B2 (en) | 2014-10-21 | 2017-09-05 | Hand Held Products, Inc. | Handheld dimensioning system with feedback |
US9557166B2 (en) | 2014-10-21 | 2017-01-31 | Hand Held Products, Inc. | Dimensioning system with multipath interference mitigation |
US9762793B2 (en) | 2014-10-21 | 2017-09-12 | Hand Held Products, Inc. | System and method for dimensioning |
US10218964B2 (en) | 2014-10-21 | 2019-02-26 | Hand Held Products, Inc. | Dimensioning system with feedback |
US10393508B2 (en) | 2014-10-21 | 2019-08-27 | Hand Held Products, Inc. | Handheld dimensioning system with measurement-conformance feedback |
US9897434B2 (en) | 2014-10-21 | 2018-02-20 | Hand Held Products, Inc. | Handheld dimensioning system with measurement-conformance feedback |
US10060729B2 (en) | 2014-10-21 | 2018-08-28 | Hand Held Products, Inc. | Handheld dimensioner with data-quality indication |
US9942474B2 (en) | 2015-04-17 | 2018-04-10 | Fotonation Cayman Limited | Systems and methods for performing high speed video capture and depth estimation using array cameras |
US11403887B2 (en) | 2015-05-19 | 2022-08-02 | Hand Held Products, Inc. | Evaluating image values |
US11906280B2 (en) | 2015-05-19 | 2024-02-20 | Hand Held Products, Inc. | Evaluating image values |
US9786101B2 (en) | 2015-05-19 | 2017-10-10 | Hand Held Products, Inc. | Evaluating image values |
US10593130B2 (en) | 2015-05-19 | 2020-03-17 | Hand Held Products, Inc. | Evaluating image values |
US10066982B2 (en) | 2015-06-16 | 2018-09-04 | Hand Held Products, Inc. | Calibrating a volume dimensioner |
US10247547B2 (en) | 2015-06-23 | 2019-04-02 | Hand Held Products, Inc. | Optical pattern projector |
US9857167B2 (en) | 2015-06-23 | 2018-01-02 | Hand Held Products, Inc. | Dual-projector three-dimensional scanner |
US9835486B2 (en) | 2015-07-07 | 2017-12-05 | Hand Held Products, Inc. | Mobile dimensioner apparatus for use in commerce |
US10612958B2 (en) | 2015-07-07 | 2020-04-07 | Hand Held Products, Inc. | Mobile dimensioner apparatus to mitigate unfair charging practices in commerce |
US10393506B2 (en) | 2015-07-15 | 2019-08-27 | Hand Held Products, Inc. | Method for a mobile dimensioning device to use a dynamic accuracy compatible with NIST standard |
US11353319B2 (en) | 2015-07-15 | 2022-06-07 | Hand Held Products, Inc. | Method for a mobile dimensioning device to use a dynamic accuracy compatible with NIST standard |
US10094650B2 (en) | 2015-07-16 | 2018-10-09 | Hand Held Products, Inc. | Dimensioning and imaging items |
US11029762B2 (en) | 2015-07-16 | 2021-06-08 | Hand Held Products, Inc. | Adjusting dimensioning results using augmented reality |
US10249030B2 (en) | 2015-10-30 | 2019-04-02 | Hand Held Products, Inc. | Image transformation for indicia reading |
US10225544B2 (en) | 2015-11-19 | 2019-03-05 | Hand Held Products, Inc. | High resolution dot pattern |
US10747227B2 (en) | 2016-01-27 | 2020-08-18 | Hand Held Products, Inc. | Vehicle positioning and object avoidance |
US10025314B2 (en) | 2016-01-27 | 2018-07-17 | Hand Held Products, Inc. | Vehicle positioning and object avoidance |
US10872214B2 (en) | 2016-06-03 | 2020-12-22 | Hand Held Products, Inc. | Wearable metrological apparatus |
US10339352B2 (en) | 2016-06-03 | 2019-07-02 | Hand Held Products, Inc. | Wearable metrological apparatus |
US9940721B2 (en) | 2016-06-10 | 2018-04-10 | Hand Held Products, Inc. | Scene change detection in a dimensioner |
US10163216B2 (en) | 2016-06-15 | 2018-12-25 | Hand Held Products, Inc. | Automatic mode switching in a volume dimensioner |
US10417769B2 (en) | 2016-06-15 | 2019-09-17 | Hand Held Products, Inc. | Automatic mode switching in a volume dimensioner |
US10909708B2 (en) | 2016-12-09 | 2021-02-02 | Hand Held Products, Inc. | Calibrating a dimensioner using ratios of measurable parameters of optic ally-perceptible geometric elements |
US11047672B2 (en) | 2017-03-28 | 2021-06-29 | Hand Held Products, Inc. | System for optically dimensioning |
EP3385686A1 (en) * | 2017-04-04 | 2018-10-10 | Hand Held Products, Inc. | Multi-spectral imaging using longitudinal chromatic aberrations |
US10798316B2 (en) | 2017-04-04 | 2020-10-06 | Hand Held Products, Inc. | Multi-spectral imaging using longitudinal chromatic aberrations |
US10733748B2 (en) | 2017-07-24 | 2020-08-04 | Hand Held Products, Inc. | Dual-pattern optical 3D dimensioning |
US11562498B2 (en) | 2017-08-21 | 2023-01-24 | Adela Imaging LLC | Systems and methods for hybrid depth regularization |
US10482618B2 (en) | 2017-08-21 | 2019-11-19 | Fotonation Limited | Systems and methods for hybrid depth regularization |
US10818026B2 (en) | 2017-08-21 | 2020-10-27 | Fotonation Limited | Systems and methods for hybrid depth regularization |
WO2019182357A1 (en) * | 2018-03-21 | 2019-09-26 | Samsung Electronics Co., Ltd. | Method for adjusting focus based on spread-level of display object and electronic device supporting the same |
US11032462B2 (en) * | 2018-03-21 | 2021-06-08 | Samsung Electronics Co., Ltd. | Method for adjusting focus based on spread-level of display object and electronic device supporting the same |
US10634559B2 (en) * | 2018-04-18 | 2020-04-28 | Raytheon Company | Spectrally-scanned hyperspectral electro-optical sensor for instantaneous situational awareness |
US20190323888A1 (en) * | 2018-04-18 | 2019-10-24 | Raytheon Company | Spectrally-scanned hyperspectral electro-optical sensor for instantaneous situational awareness |
US10584962B2 (en) | 2018-05-01 | 2020-03-10 | Hand Held Products, Inc | System and method for validating physical-item security |
US11402635B1 (en) * | 2018-05-24 | 2022-08-02 | Facebook Technologies, Llc | Systems and methods for measuring visual refractive error |
US11270110B2 (en) | 2019-09-17 | 2022-03-08 | Boston Polarimetrics, Inc. | Systems and methods for surface modeling using polarization cues |
US11699273B2 (en) | 2019-09-17 | 2023-07-11 | Intrinsic Innovation Llc | Systems and methods for surface modeling using polarization cues |
US11639846B2 (en) | 2019-09-27 | 2023-05-02 | Honeywell International Inc. | Dual-pattern optical 3D dimensioning |
US11525906B2 (en) | 2019-10-07 | 2022-12-13 | Intrinsic Innovation Llc | Systems and methods for augmentation of sensor systems and imaging systems with polarization |
US11302012B2 (en) | 2019-11-30 | 2022-04-12 | Boston Polarimetrics, Inc. | Systems and methods for transparent object segmentation using polarization cues |
US11842495B2 (en) | 2019-11-30 | 2023-12-12 | Intrinsic Innovation Llc | Systems and methods for transparent object segmentation using polarization cues |
US20210192706A1 (en) * | 2019-12-19 | 2021-06-24 | Acer Incorporated | Lens matching apparatus and lens matching method |
US11494894B2 (en) * | 2019-12-19 | 2022-11-08 | Acer Incorporated | Lens matching apparatus and lens matching method |
US11580667B2 (en) | 2020-01-29 | 2023-02-14 | Intrinsic Innovation Llc | Systems and methods for characterizing object pose detection and measurement systems |
US11797863B2 (en) | 2020-01-30 | 2023-10-24 | Intrinsic Innovation Llc | Systems and methods for synthesizing data for training statistical models on different imaging modalities including polarized images |
US11683594B2 (en) | 2021-04-15 | 2023-06-20 | Intrinsic Innovation Llc | Systems and methods for camera exposure control |
US11290658B1 (en) | 2021-04-15 | 2022-03-29 | Boston Polarimetrics, Inc. | Systems and methods for camera exposure control |
US11689813B2 (en) | 2021-07-01 | 2023-06-27 | Intrinsic Innovation Llc | Systems and methods for high dynamic range imaging using crossed polarizers |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
US20040165090A1 (en) | Auto-focus (AF) lens and process | |
US5877809A (en) | Method of automatic object detection in image | |
US6373524B2 (en) | Interchangeable lens video camera system | |
US9712755B2 (en) | Information processing method, apparatus, and program for correcting light field data | |
US8184171B2 (en) | Image pickup apparatus, image processing apparatus, image pickup method, and image processing method | |
US8749637B2 (en) | Image recognition device, focus adjustment device, image-capturing device, and image recognition method | |
CN102209245B (en) | Image processing apparatus, image pickup apparatus and image processing method | |
US7480452B2 (en) | System and method for image capturing | |
US6441855B1 (en) | Focusing device | |
US20140028839A1 (en) | Image processing method, storage medium, image processing apparatus and image pickup apparatus | |
US20020081026A1 (en) | Image retrieving apparatus | |
US7614559B2 (en) | Apparatus and method for deciding in-focus position of imaging lens | |
US5396336A (en) | In-focus detecting device | |
US9756307B2 (en) | Image processing apparatus, image pickup apparatus and image processing program for correcting color blur | |
JP2009031682A (en) | Imaging system and image signal processing program | |
JP7378219B2 (en) | Imaging device, image processing device, control method, and program | |
US7831091B2 (en) | Pattern matching system | |
CN106973199B (en) | Multi-aperture camera system for improving depth accuracy by using focusing distance scanning | |
CN109587392B (en) | Method and device for adjusting monitoring equipment, storage medium and electronic device | |
CN102158731A (en) | Image processing system and method | |
US11593958B2 (en) | Imaging device, distance measurement method, distance measurement program, and recording medium | |
EP3209014B1 (en) | Image shooting apparatus, image shooting method, image shooting program, and recording medium that records the image shooting program | |
JP2018163365A (en) | Image processing apparatus | |
JP7237450B2 (en) | Image processing device, image processing method, program, storage medium, and imaging device | |
US8676051B2 (en) | Focus detection device, focus detection method, and camera |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
STCB | Information on status: application discontinuation |
Free format text: ABANDONED -- FAILURE TO RESPOND TO AN OFFICE ACTION |